Overview

Dataset statistics

Number of variables50
Number of observations101766
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory14.2 MiB
Average record size in memory146.2 B

Variable types

Numeric13
Categorical37

Alerts

examide has constant value "No"Constant
citoglipton has constant value "No"Constant
medical_specialty has a high cardinality: 73 distinct valuesHigh cardinality
diag_1 has a high cardinality: 717 distinct valuesHigh cardinality
diag_2 has a high cardinality: 749 distinct valuesHigh cardinality
diag_3 has a high cardinality: 790 distinct valuesHigh cardinality
change is highly overall correlated with diabetesMed and 1 other fieldsHigh correlation
diabetesMed is highly overall correlated with change and 1 other fieldsHigh correlation
encounter_id is highly overall correlated with patient_nbrHigh correlation
insulin is highly overall correlated with change and 1 other fieldsHigh correlation
patient_nbr is highly overall correlated with encounter_idHigh correlation
race is highly imbalanced (55.9%)Imbalance
weight is highly imbalanced (92.0%)Imbalance
medical_specialty is highly imbalanced (54.2%)Imbalance
max_glu_serum is highly imbalanced (81.2%)Imbalance
A1Cresult is highly imbalanced (54.8%)Imbalance
metformin is highly imbalanced (59.5%)Imbalance
repaglinide is highly imbalanced (93.9%)Imbalance
nateglinide is highly imbalanced (96.9%)Imbalance
chlorpropamide is highly imbalanced (99.5%)Imbalance
glimepiride is highly imbalanced (84.0%)Imbalance
acetohexamide is highly imbalanced (> 99.9%)Imbalance
glipizide is highly imbalanced (69.2%)Imbalance
glyburide is highly imbalanced (72.3%)Imbalance
tolbutamide is highly imbalanced (99.7%)Imbalance
pioglitazone is highly imbalanced (80.2%)Imbalance
rosiglitazone is highly imbalanced (82.2%)Imbalance
acarbose is highly imbalanced (98.5%)Imbalance
miglitol is highly imbalanced (99.7%)Imbalance
troglitazone is highly imbalanced (> 99.9%)Imbalance
tolazamide is highly imbalanced (99.7%)Imbalance
glyburide-metformin is highly imbalanced (97.0%)Imbalance
glipizide-metformin is highly imbalanced (99.8%)Imbalance
glimepiride-pioglitazone is highly imbalanced (> 99.9%)Imbalance
metformin-rosiglitazone is highly imbalanced (> 99.9%)Imbalance
metformin-pioglitazone is highly imbalanced (> 99.9%)Imbalance
number_emergency is highly skewed (γ1 = 22.85558215)Skewed
encounter_id has unique valuesUnique
num_procedures has 46652 (45.8%) zerosZeros
number_outpatient has 85027 (83.6%) zerosZeros
number_emergency has 90383 (88.8%) zerosZeros
number_inpatient has 67630 (66.5%) zerosZeros

Reproduction

Analysis started2024-07-01 03:15:25.345393
Analysis finished2024-07-01 03:19:17.229436
Duration3 minutes and 51.88 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

encounter_id
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct101766
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6520165 × 108
Minimum12522
Maximum4.4386722 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size795.2 KiB
2024-07-01T03:19:17.402966image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum12522
5-th percentile27170784
Q184961194
median1.5238899 × 108
Q32.3027089 × 108
95-th percentile3.7896284 × 108
Maximum4.4386722 × 108
Range4.438547 × 108
Interquartile range (IQR)1.4530969 × 108

Descriptive statistics

Standard deviation1.026403 × 108
Coefficient of variation (CV)0.62130311
Kurtosis-0.10207139
Mean1.6520165 × 108
Median Absolute Deviation (MAD)70921143
Skewness0.69914155
Sum1.6811911 × 1013
Variance1.053503 × 1016
MonotonicityNot monotonic
2024-07-01T03:19:17.736119image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2278392 1
 
< 0.1%
190792044 1
 
< 0.1%
190790070 1
 
< 0.1%
190789722 1
 
< 0.1%
190786806 1
 
< 0.1%
190785018 1
 
< 0.1%
190781412 1
 
< 0.1%
190775886 1
 
< 0.1%
190764504 1
 
< 0.1%
190760322 1
 
< 0.1%
Other values (101756) 101756
> 99.9%
ValueCountFrequency (%)
12522 1
< 0.1%
15738 1
< 0.1%
16680 1
< 0.1%
28236 1
< 0.1%
35754 1
< 0.1%
36900 1
< 0.1%
40926 1
< 0.1%
42570 1
< 0.1%
55842 1
< 0.1%
62256 1
< 0.1%
ValueCountFrequency (%)
443867222 1
< 0.1%
443857166 1
< 0.1%
443854148 1
< 0.1%
443847782 1
< 0.1%
443847548 1
< 0.1%
443847176 1
< 0.1%
443842778 1
< 0.1%
443842340 1
< 0.1%
443842136 1
< 0.1%
443842070 1
< 0.1%

patient_nbr
Real number (ℝ)

HIGH CORRELATION 

Distinct71518
Distinct (%)70.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54330401
Minimum135
Maximum1.8950262 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size795.2 KiB
2024-07-01T03:19:18.037400image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum135
5-th percentile1456971.8
Q123413221
median45505143
Q387545950
95-th percentile1.1148027 × 108
Maximum1.8950262 × 108
Range1.8950248 × 108
Interquartile range (IQR)64132729

Descriptive statistics

Standard deviation38696359
Coefficient of variation (CV)0.71224138
Kurtosis-0.34737204
Mean54330401
Median Absolute Deviation (MAD)32950134
Skewness0.47128072
Sum5.5289876 × 1012
Variance1.4974082 × 1015
MonotonicityNot monotonic
2024-07-01T03:19:18.346703image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
88785891 40
 
< 0.1%
43140906 28
 
< 0.1%
1660293 23
 
< 0.1%
88227540 23
 
< 0.1%
23199021 23
 
< 0.1%
23643405 22
 
< 0.1%
84428613 22
 
< 0.1%
92709351 21
 
< 0.1%
88789707 20
 
< 0.1%
29903877 20
 
< 0.1%
Other values (71508) 101524
99.8%
ValueCountFrequency (%)
135 2
 
< 0.1%
378 1
 
< 0.1%
729 1
 
< 0.1%
774 1
 
< 0.1%
927 1
 
< 0.1%
1152 5
< 0.1%
1305 1
 
< 0.1%
1314 3
< 0.1%
1629 1
 
< 0.1%
2025 1
 
< 0.1%
ValueCountFrequency (%)
189502619 1
< 0.1%
189481478 1
< 0.1%
189445127 1
< 0.1%
189365864 1
< 0.1%
189351095 1
< 0.1%
189349430 1
< 0.1%
189332087 1
< 0.1%
189298877 1
< 0.1%
189257846 2
< 0.1%
189215762 1
< 0.1%

race
Categorical

IMBALANCE 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size100.1 KiB
Caucasian
76099 
AfricanAmerican
19210 
Missing
 
2273
Hispanic
 
2037
Other
 
1506

Length

Max length15
Median length9
Mean length9.983521
Min length5

Characters and Unicode

Total characters1015983
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCaucasian
2nd rowCaucasian
3rd rowAfricanAmerican
4th rowCaucasian
5th rowCaucasian

Common Values

ValueCountFrequency (%)
Caucasian 76099
74.8%
AfricanAmerican 19210
 
18.9%
Missing 2273
 
2.2%
Hispanic 2037
 
2.0%
Other 1506
 
1.5%
Asian 641
 
0.6%

Length

2024-07-01T03:19:18.641030image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-01T03:19:18.928339image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
caucasian 76099
74.8%
africanamerican 19210
 
18.9%
missing 2273
 
2.2%
hispanic 2037
 
2.0%
other 1506
 
1.5%
asian 641
 
0.6%

Most occurring characters

ValueCountFrequency (%)
a 269395
26.5%
i 123780
12.2%
n 119470
11.8%
c 116556
11.5%
s 83323
 
8.2%
C 76099
 
7.5%
u 76099
 
7.5%
r 39926
 
3.9%
A 39061
 
3.8%
e 20716
 
2.0%
Other values (9) 51558
 
5.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1015983
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 269395
26.5%
i 123780
12.2%
n 119470
11.8%
c 116556
11.5%
s 83323
 
8.2%
C 76099
 
7.5%
u 76099
 
7.5%
r 39926
 
3.9%
A 39061
 
3.8%
e 20716
 
2.0%
Other values (9) 51558
 
5.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1015983
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 269395
26.5%
i 123780
12.2%
n 119470
11.8%
c 116556
11.5%
s 83323
 
8.2%
C 76099
 
7.5%
u 76099
 
7.5%
r 39926
 
3.9%
A 39061
 
3.8%
e 20716
 
2.0%
Other values (9) 51558
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1015983
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 269395
26.5%
i 123780
12.2%
n 119470
11.8%
c 116556
11.5%
s 83323
 
8.2%
C 76099
 
7.5%
u 76099
 
7.5%
r 39926
 
3.9%
A 39061
 
3.8%
e 20716
 
2.0%
Other values (9) 51558
 
5.1%

gender
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size99.8 KiB
Female
54708 
Male
47055 
Unknown/Invalid
 
3

Length

Max length15
Median length6
Mean length5.0754967
Min length4

Characters and Unicode

Total characters516513
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowFemale
3rd rowFemale
4th rowMale
5th rowMale

Common Values

ValueCountFrequency (%)
Female 54708
53.8%
Male 47055
46.2%
Unknown/Invalid 3
 
< 0.1%

Length

2024-07-01T03:19:19.210485image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-01T03:19:19.457472image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
female 54708
53.8%
male 47055
46.2%
unknown/invalid 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 156471
30.3%
a 101766
19.7%
l 101766
19.7%
F 54708
 
10.6%
m 54708
 
10.6%
M 47055
 
9.1%
n 12
 
< 0.1%
U 3
 
< 0.1%
k 3
 
< 0.1%
o 3
 
< 0.1%
Other values (6) 18
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 516513
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 156471
30.3%
a 101766
19.7%
l 101766
19.7%
F 54708
 
10.6%
m 54708
 
10.6%
M 47055
 
9.1%
n 12
 
< 0.1%
U 3
 
< 0.1%
k 3
 
< 0.1%
o 3
 
< 0.1%
Other values (6) 18
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 516513
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 156471
30.3%
a 101766
19.7%
l 101766
19.7%
F 54708
 
10.6%
m 54708
 
10.6%
M 47055
 
9.1%
n 12
 
< 0.1%
U 3
 
< 0.1%
k 3
 
< 0.1%
o 3
 
< 0.1%
Other values (6) 18
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 516513
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 156471
30.3%
a 101766
19.7%
l 101766
19.7%
F 54708
 
10.6%
m 54708
 
10.6%
M 47055
 
9.1%
n 12
 
< 0.1%
U 3
 
< 0.1%
k 3
 
< 0.1%
o 3
 
< 0.1%
Other values (6) 18
 
< 0.1%

age
Categorical

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size100.4 KiB
[70-80)
26068 
[60-70)
22483 
[50-60)
17256 
[80-90)
17197 
[40-50)
9685 
Other values (5)
9077 

Length

Max length8
Median length7
Mean length7.0258633
Min length6

Characters and Unicode

Total characters714994
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row[0-10)
2nd row[10-20)
3rd row[20-30)
4th row[30-40)
5th row[40-50)

Common Values

ValueCountFrequency (%)
[70-80) 26068
25.6%
[60-70) 22483
22.1%
[50-60) 17256
17.0%
[80-90) 17197
16.9%
[40-50) 9685
 
9.5%
[30-40) 3775
 
3.7%
[90-100) 2793
 
2.7%
[20-30) 1657
 
1.6%
[10-20) 691
 
0.7%
[0-10) 161
 
0.2%

Length

2024-07-01T03:19:19.701268image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-01T03:19:20.027533image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
70-80 26068
25.6%
60-70 22483
22.1%
50-60 17256
17.0%
80-90 17197
16.9%
40-50 9685
 
9.5%
30-40 3775
 
3.7%
90-100 2793
 
2.7%
20-30 1657
 
1.6%
10-20 691
 
0.7%
0-10 161
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 206325
28.9%
[ 101766
14.2%
- 101766
14.2%
) 101766
14.2%
7 48551
 
6.8%
8 43265
 
6.1%
6 39739
 
5.6%
5 26941
 
3.8%
9 19990
 
2.8%
4 13460
 
1.9%
Other values (3) 11425
 
1.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 714994
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 206325
28.9%
[ 101766
14.2%
- 101766
14.2%
) 101766
14.2%
7 48551
 
6.8%
8 43265
 
6.1%
6 39739
 
5.6%
5 26941
 
3.8%
9 19990
 
2.8%
4 13460
 
1.9%
Other values (3) 11425
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 714994
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 206325
28.9%
[ 101766
14.2%
- 101766
14.2%
) 101766
14.2%
7 48551
 
6.8%
8 43265
 
6.1%
6 39739
 
5.6%
5 26941
 
3.8%
9 19990
 
2.8%
4 13460
 
1.9%
Other values (3) 11425
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 714994
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 206325
28.9%
[ 101766
14.2%
- 101766
14.2%
) 101766
14.2%
7 48551
 
6.8%
8 43265
 
6.1%
6 39739
 
5.6%
5 26941
 
3.8%
9 19990
 
2.8%
4 13460
 
1.9%
Other values (3) 11425
 
1.6%

weight
Categorical

IMBALANCE 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size100.4 KiB
Missing
98569 
[75-100)
 
1336
[50-75)
 
897
[100-125)
 
625
[125-150)
 
145
Other values (5)
 
194

Length

Max length9
Median length7
Mean length7.0286048
Min length4

Characters and Unicode

Total characters715273
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMissing
2nd rowMissing
3rd rowMissing
4th rowMissing
5th rowMissing

Common Values

ValueCountFrequency (%)
Missing 98569
96.9%
[75-100) 1336
 
1.3%
[50-75) 897
 
0.9%
[100-125) 625
 
0.6%
[125-150) 145
 
0.1%
[25-50) 97
 
0.1%
[0-25) 48
 
< 0.1%
[150-175) 35
 
< 0.1%
[175-200) 11
 
< 0.1%
>200 3
 
< 0.1%

Length

2024-07-01T03:19:20.372203image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-01T03:19:20.670112image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
missing 98569
96.9%
75-100 1336
 
1.3%
50-75 897
 
0.9%
100-125 625
 
0.6%
125-150 145
 
0.1%
25-50 97
 
0.1%
0-25 48
 
< 0.1%
150-175 35
 
< 0.1%
175-200 11
 
< 0.1%
200 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
i 197138
27.6%
s 197138
27.6%
M 98569
13.8%
n 98569
13.8%
g 98569
13.8%
0 5172
 
0.7%
5 4368
 
0.6%
[ 3194
 
0.4%
- 3194
 
0.4%
) 3194
 
0.4%
Other values (4) 6168
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 715273
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 197138
27.6%
s 197138
27.6%
M 98569
13.8%
n 98569
13.8%
g 98569
13.8%
0 5172
 
0.7%
5 4368
 
0.6%
[ 3194
 
0.4%
- 3194
 
0.4%
) 3194
 
0.4%
Other values (4) 6168
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 715273
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 197138
27.6%
s 197138
27.6%
M 98569
13.8%
n 98569
13.8%
g 98569
13.8%
0 5172
 
0.7%
5 4368
 
0.6%
[ 3194
 
0.4%
- 3194
 
0.4%
) 3194
 
0.4%
Other values (4) 6168
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 715273
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 197138
27.6%
s 197138
27.6%
M 98569
13.8%
n 98569
13.8%
g 98569
13.8%
0 5172
 
0.7%
5 4368
 
0.6%
[ 3194
 
0.4%
- 3194
 
0.4%
) 3194
 
0.4%
Other values (4) 6168
 
0.9%

admission_type_id
Real number (ℝ)

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0240061
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size795.2 KiB
2024-07-01T03:19:20.926735image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q33
95-th percentile6
Maximum8
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4454028
Coefficient of variation (CV)0.7141297
Kurtosis1.9424761
Mean2.0240061
Median Absolute Deviation (MAD)0
Skewness1.5919843
Sum205975
Variance2.0891893
MonotonicityNot monotonic
2024-07-01T03:19:21.150884image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 53990
53.1%
3 18869
 
18.5%
2 18480
 
18.2%
6 5291
 
5.2%
5 4785
 
4.7%
8 320
 
0.3%
7 21
 
< 0.1%
4 10
 
< 0.1%
ValueCountFrequency (%)
1 53990
53.1%
2 18480
 
18.2%
3 18869
 
18.5%
4 10
 
< 0.1%
5 4785
 
4.7%
6 5291
 
5.2%
7 21
 
< 0.1%
8 320
 
0.3%
ValueCountFrequency (%)
8 320
 
0.3%
7 21
 
< 0.1%
6 5291
 
5.2%
5 4785
 
4.7%
4 10
 
< 0.1%
3 18869
 
18.5%
2 18480
 
18.2%
1 53990
53.1%

discharge_disposition_id
Real number (ℝ)

Distinct26
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7156418
Minimum1
Maximum28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size795.2 KiB
2024-07-01T03:19:21.405006image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q34
95-th percentile18
Maximum28
Range27
Interquartile range (IQR)3

Descriptive statistics

Standard deviation5.2801655
Coefficient of variation (CV)1.4210642
Kurtosis6.0033468
Mean3.7156418
Median Absolute Deviation (MAD)0
Skewness2.563067
Sum378126
Variance27.880148
MonotonicityNot monotonic
2024-07-01T03:19:21.664092image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
1 60234
59.2%
3 13954
 
13.7%
6 12902
 
12.7%
18 3691
 
3.6%
2 2128
 
2.1%
22 1993
 
2.0%
11 1642
 
1.6%
5 1184
 
1.2%
25 989
 
1.0%
4 815
 
0.8%
Other values (16) 2234
 
2.2%
ValueCountFrequency (%)
1 60234
59.2%
2 2128
 
2.1%
3 13954
 
13.7%
4 815
 
0.8%
5 1184
 
1.2%
6 12902
 
12.7%
7 623
 
0.6%
8 108
 
0.1%
9 21
 
< 0.1%
10 6
 
< 0.1%
ValueCountFrequency (%)
28 139
 
0.1%
27 5
 
< 0.1%
25 989
 
1.0%
24 48
 
< 0.1%
23 412
 
0.4%
22 1993
2.0%
20 2
 
< 0.1%
19 8
 
< 0.1%
18 3691
3.6%
17 14
 
< 0.1%

admission_source_id
Real number (ℝ)

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7544366
Minimum1
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size795.2 KiB
2024-07-01T03:19:21.912961image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median7
Q37
95-th percentile17
Maximum25
Range24
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.0640808
Coefficient of variation (CV)0.70625173
Kurtosis1.7449894
Mean5.7544366
Median Absolute Deviation (MAD)0
Skewness1.0299349
Sum585606
Variance16.516753
MonotonicityNot monotonic
2024-07-01T03:19:22.168726image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
7 57494
56.5%
1 29565
29.1%
17 6781
 
6.7%
4 3187
 
3.1%
6 2264
 
2.2%
2 1104
 
1.1%
5 855
 
0.8%
3 187
 
0.2%
20 161
 
0.2%
9 125
 
0.1%
Other values (7) 43
 
< 0.1%
ValueCountFrequency (%)
1 29565
29.1%
2 1104
 
1.1%
3 187
 
0.2%
4 3187
 
3.1%
5 855
 
0.8%
6 2264
 
2.2%
7 57494
56.5%
8 16
 
< 0.1%
9 125
 
0.1%
10 8
 
< 0.1%
ValueCountFrequency (%)
25 2
 
< 0.1%
22 12
 
< 0.1%
20 161
 
0.2%
17 6781
6.7%
14 2
 
< 0.1%
13 1
 
< 0.1%
11 2
 
< 0.1%
10 8
 
< 0.1%
9 125
 
0.1%
8 16
 
< 0.1%

time_in_hospital
Real number (ℝ)

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.3959869
Minimum1
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size795.2 KiB
2024-07-01T03:19:22.402621image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile11
Maximum14
Range13
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.9851078
Coefficient of variation (CV)0.67905293
Kurtosis0.85025084
Mean4.3959869
Median Absolute Deviation (MAD)2
Skewness1.1339987
Sum447362
Variance8.9108684
MonotonicityNot monotonic
2024-07-01T03:19:22.716391image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
3 17756
17.4%
2 17224
16.9%
1 14208
14.0%
4 13924
13.7%
5 9966
9.8%
6 7539
7.4%
7 5859
 
5.8%
8 4391
 
4.3%
9 3002
 
2.9%
10 2342
 
2.3%
Other values (4) 5555
 
5.5%
ValueCountFrequency (%)
1 14208
14.0%
2 17224
16.9%
3 17756
17.4%
4 13924
13.7%
5 9966
9.8%
6 7539
7.4%
7 5859
 
5.8%
8 4391
 
4.3%
9 3002
 
2.9%
10 2342
 
2.3%
ValueCountFrequency (%)
14 1042
 
1.0%
13 1210
 
1.2%
12 1448
 
1.4%
11 1855
 
1.8%
10 2342
 
2.3%
9 3002
 
2.9%
8 4391
4.3%
7 5859
5.8%
6 7539
7.4%
5 9966
9.8%

payer_code
Categorical

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size101.1 KiB
Missing
40256 
MC
32439 
HM
6274 
SP
5007 
BC
4655 
Other values (13)
13135 

Length

Max length7
Median length2
Mean length3.9778708
Min length2

Characters and Unicode

Total characters404812
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowMissing
2nd rowMissing
3rd rowMissing
4th rowMissing
5th rowMissing

Common Values

ValueCountFrequency (%)
Missing 40256
39.6%
MC 32439
31.9%
HM 6274
 
6.2%
SP 5007
 
4.9%
BC 4655
 
4.6%
MD 3532
 
3.5%
CP 2533
 
2.5%
UN 2448
 
2.4%
CM 1937
 
1.9%
OG 1033
 
1.0%
Other values (8) 1652
 
1.6%

Length

2024-07-01T03:19:23.183010image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
missing 40256
39.6%
mc 32439
31.9%
hm 6274
 
6.2%
sp 5007
 
4.9%
bc 4655
 
4.6%
md 3532
 
3.5%
cp 2533
 
2.5%
un 2448
 
2.4%
cm 1937
 
1.9%
og 1033
 
1.0%
Other values (8) 1652
 
1.6%

Most occurring characters

ValueCountFrequency (%)
M 85066
21.0%
i 80512
19.9%
s 80512
19.9%
C 41845
10.3%
n 40256
9.9%
g 40256
9.9%
P 8211
 
2.0%
H 6420
 
1.6%
S 5062
 
1.3%
B 4655
 
1.1%
Other values (10) 12017
 
3.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 404812
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M 85066
21.0%
i 80512
19.9%
s 80512
19.9%
C 41845
10.3%
n 40256
9.9%
g 40256
9.9%
P 8211
 
2.0%
H 6420
 
1.6%
S 5062
 
1.3%
B 4655
 
1.1%
Other values (10) 12017
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 404812
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M 85066
21.0%
i 80512
19.9%
s 80512
19.9%
C 41845
10.3%
n 40256
9.9%
g 40256
9.9%
P 8211
 
2.0%
H 6420
 
1.6%
S 5062
 
1.3%
B 4655
 
1.1%
Other values (10) 12017
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 404812
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M 85066
21.0%
i 80512
19.9%
s 80512
19.9%
C 41845
10.3%
n 40256
9.9%
g 40256
9.9%
P 8211
 
2.0%
H 6420
 
1.6%
S 5062
 
1.3%
B 4655
 
1.1%
Other values (10) 12017
 
3.0%

medical_specialty
Categorical

HIGH CARDINALITY  IMBALANCE 

Distinct73
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size106.8 KiB
Missing
49949 
InternalMedicine
14635 
Emergency/Trauma
7565 
Family/GeneralPractice
7440 
Cardiology
5352 
Other values (68)
16825 

Length

Max length36
Median length33
Mean length11.557603
Min length6

Characters and Unicode

Total characters1176171
Distinct characters43
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)< 0.1%

Sample

1st rowPediatrics-Endocrinology
2nd rowMissing
3rd rowMissing
4th rowMissing
5th rowMissing

Common Values

ValueCountFrequency (%)
Missing 49949
49.1%
InternalMedicine 14635
 
14.4%
Emergency/Trauma 7565
 
7.4%
Family/GeneralPractice 7440
 
7.3%
Cardiology 5352
 
5.3%
Surgery-General 3099
 
3.0%
Nephrology 1613
 
1.6%
Orthopedics 1400
 
1.4%
Orthopedics-Reconstructive 1233
 
1.2%
Radiologist 1140
 
1.1%
Other values (63) 8340
 
8.2%

Length

2024-07-01T03:19:23.639600image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
missing 49949
49.1%
internalmedicine 14635
 
14.4%
emergency/trauma 7565
 
7.4%
family/generalpractice 7440
 
7.3%
cardiology 5352
 
5.3%
surgery-general 3099
 
3.0%
nephrology 1613
 
1.6%
orthopedics 1400
 
1.4%
orthopedics-reconstructive 1233
 
1.2%
radiologist 1140
 
1.1%
Other values (63) 8340
 
8.2%

Most occurring characters

ValueCountFrequency (%)
i 163206
13.9%
n 118747
10.1%
s 110536
 
9.4%
e 105151
 
8.9%
r 76899
 
6.5%
g 75545
 
6.4%
a 71149
 
6.0%
M 65004
 
5.5%
c 50007
 
4.3%
l 48871
 
4.2%
Other values (33) 291056
24.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1176171
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 163206
13.9%
n 118747
10.1%
s 110536
 
9.4%
e 105151
 
8.9%
r 76899
 
6.5%
g 75545
 
6.4%
a 71149
 
6.0%
M 65004
 
5.5%
c 50007
 
4.3%
l 48871
 
4.2%
Other values (33) 291056
24.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1176171
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 163206
13.9%
n 118747
10.1%
s 110536
 
9.4%
e 105151
 
8.9%
r 76899
 
6.5%
g 75545
 
6.4%
a 71149
 
6.0%
M 65004
 
5.5%
c 50007
 
4.3%
l 48871
 
4.2%
Other values (33) 291056
24.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1176171
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 163206
13.9%
n 118747
10.1%
s 110536
 
9.4%
e 105151
 
8.9%
r 76899
 
6.5%
g 75545
 
6.4%
a 71149
 
6.0%
M 65004
 
5.5%
c 50007
 
4.3%
l 48871
 
4.2%
Other values (33) 291056
24.7%

num_lab_procedures
Real number (ℝ)

Distinct118
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.095641
Minimum1
Maximum132
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size795.2 KiB
2024-07-01T03:19:24.688123image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q131
median44
Q357
95-th percentile73
Maximum132
Range131
Interquartile range (IQR)26

Descriptive statistics

Standard deviation19.674362
Coefficient of variation (CV)0.45652789
Kurtosis-0.24507352
Mean43.095641
Median Absolute Deviation (MAD)13
Skewness-0.23654392
Sum4385671
Variance387.08053
MonotonicityNot monotonic
2024-07-01T03:19:25.128504image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 3208
 
3.2%
43 2804
 
2.8%
44 2496
 
2.5%
45 2376
 
2.3%
38 2213
 
2.2%
40 2201
 
2.2%
46 2189
 
2.2%
41 2117
 
2.1%
42 2113
 
2.1%
47 2106
 
2.1%
Other values (108) 77943
76.6%
ValueCountFrequency (%)
1 3208
3.2%
2 1101
 
1.1%
3 668
 
0.7%
4 378
 
0.4%
5 286
 
0.3%
6 282
 
0.3%
7 323
 
0.3%
8 366
 
0.4%
9 933
 
0.9%
10 838
 
0.8%
ValueCountFrequency (%)
132 1
 
< 0.1%
129 1
 
< 0.1%
126 1
 
< 0.1%
121 1
 
< 0.1%
120 1
 
< 0.1%
118 1
 
< 0.1%
114 2
< 0.1%
113 3
< 0.1%
111 3
< 0.1%
109 4
< 0.1%

num_procedures
Real number (ℝ)

ZEROS 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3397304
Minimum0
Maximum6
Zeros46652
Zeros (%)45.8%
Negative0
Negative (%)0.0%
Memory size795.2 KiB
2024-07-01T03:19:25.614223image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.705807
Coefficient of variation (CV)1.2732465
Kurtosis0.8571103
Mean1.3397304
Median Absolute Deviation (MAD)1
Skewness1.3164148
Sum136339
Variance2.9097775
MonotonicityNot monotonic
2024-07-01T03:19:26.046031image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 46652
45.8%
1 20742
20.4%
2 12717
 
12.5%
3 9443
 
9.3%
6 4954
 
4.9%
4 4180
 
4.1%
5 3078
 
3.0%
ValueCountFrequency (%)
0 46652
45.8%
1 20742
20.4%
2 12717
 
12.5%
3 9443
 
9.3%
4 4180
 
4.1%
5 3078
 
3.0%
6 4954
 
4.9%
ValueCountFrequency (%)
6 4954
 
4.9%
5 3078
 
3.0%
4 4180
 
4.1%
3 9443
 
9.3%
2 12717
 
12.5%
1 20742
20.4%
0 46652
45.8%

num_medications
Real number (ℝ)

Distinct75
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.021844
Minimum1
Maximum81
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size795.2 KiB
2024-07-01T03:19:26.331486image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q110
median15
Q320
95-th percentile31
Maximum81
Range80
Interquartile range (IQR)10

Descriptive statistics

Standard deviation8.1275662
Coefficient of variation (CV)0.50728032
Kurtosis3.4681549
Mean16.021844
Median Absolute Deviation (MAD)5
Skewness1.3266721
Sum1630479
Variance66.057332
MonotonicityNot monotonic
2024-07-01T03:19:26.660838image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13 6086
 
6.0%
12 6004
 
5.9%
11 5795
 
5.7%
15 5792
 
5.7%
14 5707
 
5.6%
16 5430
 
5.3%
10 5346
 
5.3%
17 4919
 
4.8%
9 4913
 
4.8%
18 4523
 
4.4%
Other values (65) 47251
46.4%
ValueCountFrequency (%)
1 262
 
0.3%
2 470
 
0.5%
3 900
 
0.9%
4 1417
 
1.4%
5 2017
 
2.0%
6 2699
2.7%
7 3484
3.4%
8 4353
4.3%
9 4913
4.8%
10 5346
5.3%
ValueCountFrequency (%)
81 1
 
< 0.1%
79 1
 
< 0.1%
75 2
 
< 0.1%
74 1
 
< 0.1%
72 3
< 0.1%
70 2
 
< 0.1%
69 5
< 0.1%
68 7
< 0.1%
67 7
< 0.1%
66 5
< 0.1%

number_outpatient
Real number (ℝ)

ZEROS 

Distinct39
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.36935715
Minimum0
Maximum42
Zeros85027
Zeros (%)83.6%
Negative0
Negative (%)0.0%
Memory size795.2 KiB
2024-07-01T03:19:26.965137image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum42
Range42
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.2672651
Coefficient of variation (CV)3.4310019
Kurtosis147.90774
Mean0.36935715
Median Absolute Deviation (MAD)0
Skewness8.8329589
Sum37588
Variance1.6059608
MonotonicityNot monotonic
2024-07-01T03:19:27.216522image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
0 85027
83.6%
1 8547
 
8.4%
2 3594
 
3.5%
3 2042
 
2.0%
4 1099
 
1.1%
5 533
 
0.5%
6 303
 
0.3%
7 155
 
0.2%
8 98
 
0.1%
9 83
 
0.1%
Other values (29) 285
 
0.3%
ValueCountFrequency (%)
0 85027
83.6%
1 8547
 
8.4%
2 3594
 
3.5%
3 2042
 
2.0%
4 1099
 
1.1%
5 533
 
0.5%
6 303
 
0.3%
7 155
 
0.2%
8 98
 
0.1%
9 83
 
0.1%
ValueCountFrequency (%)
42 1
< 0.1%
40 1
< 0.1%
39 1
< 0.1%
38 1
< 0.1%
37 1
< 0.1%
36 2
< 0.1%
35 2
< 0.1%
34 1
< 0.1%
33 2
< 0.1%
29 2
< 0.1%

number_emergency
Real number (ℝ)

SKEWED  ZEROS 

Distinct33
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.19783621
Minimum0
Maximum76
Zeros90383
Zeros (%)88.8%
Negative0
Negative (%)0.0%
Memory size795.2 KiB
2024-07-01T03:19:27.486964image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum76
Range76
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.93047227
Coefficient of variation (CV)4.7032455
Kurtosis1191.6867
Mean0.19783621
Median Absolute Deviation (MAD)0
Skewness22.855582
Sum20133
Variance0.86577864
MonotonicityNot monotonic
2024-07-01T03:19:27.742890image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0 90383
88.8%
1 7677
 
7.5%
2 2042
 
2.0%
3 725
 
0.7%
4 374
 
0.4%
5 192
 
0.2%
6 94
 
0.1%
7 73
 
0.1%
8 50
 
< 0.1%
10 34
 
< 0.1%
Other values (23) 122
 
0.1%
ValueCountFrequency (%)
0 90383
88.8%
1 7677
 
7.5%
2 2042
 
2.0%
3 725
 
0.7%
4 374
 
0.4%
5 192
 
0.2%
6 94
 
0.1%
7 73
 
0.1%
8 50
 
< 0.1%
9 33
 
< 0.1%
ValueCountFrequency (%)
76 1
< 0.1%
64 1
< 0.1%
63 1
< 0.1%
54 1
< 0.1%
46 1
< 0.1%
42 1
< 0.1%
37 1
< 0.1%
29 1
< 0.1%
28 1
< 0.1%
25 2
< 0.1%

number_inpatient
Real number (ℝ)

ZEROS 

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.63556591
Minimum0
Maximum21
Zeros67630
Zeros (%)66.5%
Negative0
Negative (%)0.0%
Memory size795.2 KiB
2024-07-01T03:19:28.010983image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum21
Range21
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2628633
Coefficient of variation (CV)1.9869903
Kurtosis20.719397
Mean0.63556591
Median Absolute Deviation (MAD)0
Skewness3.614139
Sum64679
Variance1.5948237
MonotonicityNot monotonic
2024-07-01T03:19:28.242047image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 67630
66.5%
1 19521
 
19.2%
2 7566
 
7.4%
3 3411
 
3.4%
4 1622
 
1.6%
5 812
 
0.8%
6 480
 
0.5%
7 268
 
0.3%
8 151
 
0.1%
9 111
 
0.1%
Other values (11) 194
 
0.2%
ValueCountFrequency (%)
0 67630
66.5%
1 19521
 
19.2%
2 7566
 
7.4%
3 3411
 
3.4%
4 1622
 
1.6%
5 812
 
0.8%
6 480
 
0.5%
7 268
 
0.3%
8 151
 
0.1%
9 111
 
0.1%
ValueCountFrequency (%)
21 1
 
< 0.1%
19 2
 
< 0.1%
18 1
 
< 0.1%
17 1
 
< 0.1%
16 6
 
< 0.1%
15 9
 
< 0.1%
14 10
 
< 0.1%
13 20
< 0.1%
12 34
< 0.1%
11 49
< 0.1%

diag_1
Categorical

HIGH CARDINALITY 

Distinct717
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size257.1 KiB
428
 
6862
414
 
6581
786
 
4016
410
 
3614
486
 
3508
Other values (712)
77185 

Length

Max length7
Median length3
Mean length3.1764538
Min length1

Characters and Unicode

Total characters323255
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique82 ?
Unique (%)0.1%

Sample

1st row250.83
2nd row276
3rd row648
4th row8
5th row197

Common Values

ValueCountFrequency (%)
428 6862
 
6.7%
414 6581
 
6.5%
786 4016
 
3.9%
410 3614
 
3.6%
486 3508
 
3.4%
427 2766
 
2.7%
491 2275
 
2.2%
715 2151
 
2.1%
682 2042
 
2.0%
434 2028
 
2.0%
Other values (707) 65923
64.8%

Length

2024-07-01T03:19:28.518747image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
428 6862
 
6.7%
414 6581
 
6.5%
786 4016
 
3.9%
410 3614
 
3.6%
486 3508
 
3.4%
427 2766
 
2.7%
491 2275
 
2.2%
715 2151
 
2.1%
682 2042
 
2.0%
434 2028
 
2.0%
Other values (707) 65923
64.8%

Most occurring characters

ValueCountFrequency (%)
4 55457
17.2%
2 39876
12.3%
8 37949
11.7%
5 37131
11.5%
7 28668
8.9%
1 28106
8.7%
0 24960
7.7%
6 23198
7.2%
9 19978
 
6.2%
3 17618
 
5.5%
Other values (8) 10314
 
3.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 323255
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 55457
17.2%
2 39876
12.3%
8 37949
11.7%
5 37131
11.5%
7 28668
8.9%
1 28106
8.7%
0 24960
7.7%
6 23198
7.2%
9 19978
 
6.2%
3 17618
 
5.5%
Other values (8) 10314
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 323255
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 55457
17.2%
2 39876
12.3%
8 37949
11.7%
5 37131
11.5%
7 28668
8.9%
1 28106
8.7%
0 24960
7.7%
6 23198
7.2%
9 19978
 
6.2%
3 17618
 
5.5%
Other values (8) 10314
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 323255
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 55457
17.2%
2 39876
12.3%
8 37949
11.7%
5 37131
11.5%
7 28668
8.9%
1 28106
8.7%
0 24960
7.7%
6 23198
7.2%
9 19978
 
6.2%
3 17618
 
5.5%
Other values (8) 10314
 
3.2%

diag_2
Categorical

HIGH CARDINALITY 

Distinct749
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size259.1 KiB
276
 
6752
428
 
6662
250
 
6071
427
 
5036
401
 
3736
Other values (744)
73509 

Length

Max length7
Median length3
Mean length3.1873022
Min length1

Characters and Unicode

Total characters324359
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique124 ?
Unique (%)0.1%

Sample

1st rowMissing
2nd row250.01
3rd row250
4th row250.43
5th row157

Common Values

ValueCountFrequency (%)
276 6752
 
6.6%
428 6662
 
6.5%
250 6071
 
6.0%
427 5036
 
4.9%
401 3736
 
3.7%
496 3305
 
3.2%
599 3288
 
3.2%
403 2823
 
2.8%
414 2650
 
2.6%
411 2566
 
2.5%
Other values (739) 58877
57.9%

Length

2024-07-01T03:19:28.780698image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
276 6752
 
6.6%
428 6662
 
6.5%
250 6071
 
6.0%
427 5036
 
4.9%
401 3736
 
3.7%
496 3305
 
3.2%
599 3288
 
3.2%
403 2823
 
2.8%
414 2650
 
2.6%
411 2566
 
2.5%
Other values (739) 58877
57.9%

Most occurring characters

ValueCountFrequency (%)
4 51155
15.8%
2 49765
15.3%
5 38176
11.8%
0 34046
10.5%
8 28711
8.9%
7 28654
8.8%
1 26158
8.1%
9 21842
6.7%
6 19990
 
6.2%
3 14097
 
4.3%
Other values (8) 11765
 
3.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 324359
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 51155
15.8%
2 49765
15.3%
5 38176
11.8%
0 34046
10.5%
8 28711
8.9%
7 28654
8.8%
1 26158
8.1%
9 21842
6.7%
6 19990
 
6.2%
3 14097
 
4.3%
Other values (8) 11765
 
3.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 324359
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 51155
15.8%
2 49765
15.3%
5 38176
11.8%
0 34046
10.5%
8 28711
8.9%
7 28654
8.8%
1 26158
8.1%
9 21842
6.7%
6 19990
 
6.2%
3 14097
 
4.3%
Other values (8) 11765
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 324359
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 51155
15.8%
2 49765
15.3%
5 38176
11.8%
0 34046
10.5%
8 28711
8.9%
7 28654
8.8%
1 26158
8.1%
9 21842
6.7%
6 19990
 
6.2%
3 14097
 
4.3%
Other values (8) 11765
 
3.6%

diag_3
Categorical

HIGH CARDINALITY 

Distinct790
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size277.6 KiB
250
11555 
401
8289 
276
 
5175
428
 
4577
427
 
3955
Other values (785)
68215 

Length

Max length7
Median length3
Mean length3.1955565
Min length1

Characters and Unicode

Total characters325199
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique122 ?
Unique (%)0.1%

Sample

1st rowMissing
2nd row255
3rd rowV27
4th row403
5th row250

Common Values

ValueCountFrequency (%)
250 11555
 
11.4%
401 8289
 
8.1%
276 5175
 
5.1%
428 4577
 
4.5%
427 3955
 
3.9%
414 3664
 
3.6%
496 2605
 
2.6%
403 2357
 
2.3%
585 1992
 
2.0%
272 1969
 
1.9%
Other values (780) 55628
54.7%

Length

2024-07-01T03:19:29.026820image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
250 11555
 
11.4%
401 8289
 
8.1%
276 5175
 
5.1%
428 4577
 
4.5%
427 3955
 
3.9%
414 3664
 
3.6%
496 2605
 
2.6%
403 2357
 
2.3%
585 1992
 
2.0%
272 1969
 
1.9%
Other values (780) 55628
54.7%

Most occurring characters

ValueCountFrequency (%)
2 51244
15.8%
4 49252
15.1%
5 41260
12.7%
0 39711
12.2%
7 26504
8.2%
1 24684
7.6%
8 23825
7.3%
9 17323
 
5.3%
6 16441
 
5.1%
3 14333
 
4.4%
Other values (8) 20622
6.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 325199
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 51244
15.8%
4 49252
15.1%
5 41260
12.7%
0 39711
12.2%
7 26504
8.2%
1 24684
7.6%
8 23825
7.3%
9 17323
 
5.3%
6 16441
 
5.1%
3 14333
 
4.4%
Other values (8) 20622
6.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 325199
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 51244
15.8%
4 49252
15.1%
5 41260
12.7%
0 39711
12.2%
7 26504
8.2%
1 24684
7.6%
8 23825
7.3%
9 17323
 
5.3%
6 16441
 
5.1%
3 14333
 
4.4%
Other values (8) 20622
6.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 325199
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 51244
15.8%
4 49252
15.1%
5 41260
12.7%
0 39711
12.2%
7 26504
8.2%
1 24684
7.6%
8 23825
7.3%
9 17323
 
5.3%
6 16441
 
5.1%
3 14333
 
4.4%
Other values (8) 20622
6.3%

number_diagnoses
Real number (ℝ)

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.4226068
Minimum1
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size795.2 KiB
2024-07-01T03:19:29.268378image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q16
median8
Q39
95-th percentile9
Maximum16
Range15
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.9336001
Coefficient of variation (CV)0.26050149
Kurtosis-0.079056024
Mean7.4226068
Median Absolute Deviation (MAD)1
Skewness-0.87674624
Sum755369
Variance3.7388095
MonotonicityNot monotonic
2024-07-01T03:19:29.502227image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
9 49474
48.6%
5 11393
 
11.2%
8 10616
 
10.4%
7 10393
 
10.2%
6 10161
 
10.0%
4 5537
 
5.4%
3 2835
 
2.8%
2 1023
 
1.0%
1 219
 
0.2%
16 45
 
< 0.1%
Other values (6) 70
 
0.1%
ValueCountFrequency (%)
1 219
 
0.2%
2 1023
 
1.0%
3 2835
 
2.8%
4 5537
 
5.4%
5 11393
 
11.2%
6 10161
 
10.0%
7 10393
 
10.2%
8 10616
 
10.4%
9 49474
48.6%
10 17
 
< 0.1%
ValueCountFrequency (%)
16 45
 
< 0.1%
15 10
 
< 0.1%
14 7
 
< 0.1%
13 16
 
< 0.1%
12 9
 
< 0.1%
11 11
 
< 0.1%
10 17
 
< 0.1%
9 49474
48.6%
8 10616
 
10.4%
7 10393
 
10.2%

max_glu_serum
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size99.9 KiB
No Test
96420 
Norm
 
2597
>200
 
1485
>300
 
1264

Length

Max length7
Median length7
Mean length6.8424032
Min length4

Characters and Unicode

Total characters696324
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo Test
2nd rowNo Test
3rd rowNo Test
4th rowNo Test
5th rowNo Test

Common Values

ValueCountFrequency (%)
No Test 96420
94.7%
Norm 2597
 
2.6%
>200 1485
 
1.5%
>300 1264
 
1.2%

Length

2024-07-01T03:19:29.770141image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-01T03:19:30.039898image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 96420
48.7%
test 96420
48.7%
norm 2597
 
1.3%
200 1485
 
0.7%
300 1264
 
0.6%

Most occurring characters

ValueCountFrequency (%)
N 99017
14.2%
o 99017
14.2%
96420
13.8%
T 96420
13.8%
e 96420
13.8%
s 96420
13.8%
t 96420
13.8%
0 5498
 
0.8%
> 2749
 
0.4%
r 2597
 
0.4%
Other values (3) 5346
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 696324
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 99017
14.2%
o 99017
14.2%
96420
13.8%
T 96420
13.8%
e 96420
13.8%
s 96420
13.8%
t 96420
13.8%
0 5498
 
0.8%
> 2749
 
0.4%
r 2597
 
0.4%
Other values (3) 5346
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 696324
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 99017
14.2%
o 99017
14.2%
96420
13.8%
T 96420
13.8%
e 96420
13.8%
s 96420
13.8%
t 96420
13.8%
0 5498
 
0.8%
> 2749
 
0.4%
r 2597
 
0.4%
Other values (3) 5346
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 696324
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 99017
14.2%
o 99017
14.2%
96420
13.8%
T 96420
13.8%
e 96420
13.8%
s 96420
13.8%
t 96420
13.8%
0 5498
 
0.8%
> 2749
 
0.4%
r 2597
 
0.4%
Other values (3) 5346
 
0.8%

A1Cresult
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size99.9 KiB
No Test
84748 
>8
 
8216
Norm
 
4990
>7
 
3812

Length

Max length7
Median length7
Mean length6.2619342
Min length2

Characters and Unicode

Total characters637252
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo Test
2nd rowNo Test
3rd rowNo Test
4th rowNo Test
5th rowNo Test

Common Values

ValueCountFrequency (%)
No Test 84748
83.3%
>8 8216
 
8.1%
Norm 4990
 
4.9%
>7 3812
 
3.7%

Length

2024-07-01T03:19:30.258435image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-01T03:19:30.524136image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 84748
45.4%
test 84748
45.4%
8 8216
 
4.4%
norm 4990
 
2.7%
7 3812
 
2.0%

Most occurring characters

ValueCountFrequency (%)
N 89738
14.1%
o 89738
14.1%
84748
13.3%
T 84748
13.3%
e 84748
13.3%
s 84748
13.3%
t 84748
13.3%
> 12028
 
1.9%
8 8216
 
1.3%
r 4990
 
0.8%
Other values (2) 8802
 
1.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 637252
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 89738
14.1%
o 89738
14.1%
84748
13.3%
T 84748
13.3%
e 84748
13.3%
s 84748
13.3%
t 84748
13.3%
> 12028
 
1.9%
8 8216
 
1.3%
r 4990
 
0.8%
Other values (2) 8802
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 637252
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 89738
14.1%
o 89738
14.1%
84748
13.3%
T 84748
13.3%
e 84748
13.3%
s 84748
13.3%
t 84748
13.3%
> 12028
 
1.9%
8 8216
 
1.3%
r 4990
 
0.8%
Other values (2) 8802
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 637252
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 89738
14.1%
o 89738
14.1%
84748
13.3%
T 84748
13.3%
e 84748
13.3%
s 84748
13.3%
t 84748
13.3%
> 12028
 
1.9%
8 8216
 
1.3%
r 4990
 
0.8%
Other values (2) 8802
 
1.4%

metformin
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size99.9 KiB
No
81778 
Steady
18346 
Up
 
1067
Down
 
575

Length

Max length6
Median length2
Mean length2.7324057
Min length2

Characters and Unicode

Total characters278066
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 81778
80.4%
Steady 18346
 
18.0%
Up 1067
 
1.0%
Down 575
 
0.6%

Length

2024-07-01T03:19:30.762679image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-01T03:19:31.046507image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 81778
80.4%
steady 18346
 
18.0%
up 1067
 
1.0%
down 575
 
0.6%

Most occurring characters

ValueCountFrequency (%)
o 82353
29.6%
N 81778
29.4%
S 18346
 
6.6%
t 18346
 
6.6%
e 18346
 
6.6%
a 18346
 
6.6%
d 18346
 
6.6%
y 18346
 
6.6%
U 1067
 
0.4%
p 1067
 
0.4%
Other values (3) 1725
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 278066
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 82353
29.6%
N 81778
29.4%
S 18346
 
6.6%
t 18346
 
6.6%
e 18346
 
6.6%
a 18346
 
6.6%
d 18346
 
6.6%
y 18346
 
6.6%
U 1067
 
0.4%
p 1067
 
0.4%
Other values (3) 1725
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 278066
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 82353
29.6%
N 81778
29.4%
S 18346
 
6.6%
t 18346
 
6.6%
e 18346
 
6.6%
a 18346
 
6.6%
d 18346
 
6.6%
y 18346
 
6.6%
U 1067
 
0.4%
p 1067
 
0.4%
Other values (3) 1725
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 278066
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 82353
29.6%
N 81778
29.4%
S 18346
 
6.6%
t 18346
 
6.6%
e 18346
 
6.6%
a 18346
 
6.6%
d 18346
 
6.6%
y 18346
 
6.6%
U 1067
 
0.4%
p 1067
 
0.4%
Other values (3) 1725
 
0.6%

repaglinide
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size99.9 KiB
No
100227 
Steady
 
1384
Up
 
110
Down
 
45

Length

Max length6
Median length2
Mean length2.0552837
Min length2

Characters and Unicode

Total characters209158
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 100227
98.5%
Steady 1384
 
1.4%
Up 110
 
0.1%
Down 45
 
< 0.1%

Length

2024-07-01T03:19:31.297084image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-01T03:19:31.602974image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 100227
98.5%
steady 1384
 
1.4%
up 110
 
0.1%
down 45
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
o 100272
47.9%
N 100227
47.9%
S 1384
 
0.7%
t 1384
 
0.7%
e 1384
 
0.7%
a 1384
 
0.7%
d 1384
 
0.7%
y 1384
 
0.7%
U 110
 
0.1%
p 110
 
0.1%
Other values (3) 135
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 209158
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 100272
47.9%
N 100227
47.9%
S 1384
 
0.7%
t 1384
 
0.7%
e 1384
 
0.7%
a 1384
 
0.7%
d 1384
 
0.7%
y 1384
 
0.7%
U 110
 
0.1%
p 110
 
0.1%
Other values (3) 135
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 209158
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 100272
47.9%
N 100227
47.9%
S 1384
 
0.7%
t 1384
 
0.7%
e 1384
 
0.7%
a 1384
 
0.7%
d 1384
 
0.7%
y 1384
 
0.7%
U 110
 
0.1%
p 110
 
0.1%
Other values (3) 135
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 209158
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 100272
47.9%
N 100227
47.9%
S 1384
 
0.7%
t 1384
 
0.7%
e 1384
 
0.7%
a 1384
 
0.7%
d 1384
 
0.7%
y 1384
 
0.7%
U 110
 
0.1%
p 110
 
0.1%
Other values (3) 135
 
0.1%

nateglinide
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size99.9 KiB
No
101063 
Steady
 
668
Up
 
24
Down
 
11

Length

Max length6
Median length2
Mean length2.0264725
Min length2

Characters and Unicode

Total characters206226
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 101063
99.3%
Steady 668
 
0.7%
Up 24
 
< 0.1%
Down 11
 
< 0.1%

Length

2024-07-01T03:19:31.847400image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-01T03:19:32.117374image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 101063
99.3%
steady 668
 
0.7%
up 24
 
< 0.1%
down 11
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
o 101074
49.0%
N 101063
49.0%
S 668
 
0.3%
t 668
 
0.3%
e 668
 
0.3%
a 668
 
0.3%
d 668
 
0.3%
y 668
 
0.3%
U 24
 
< 0.1%
p 24
 
< 0.1%
Other values (3) 33
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 206226
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 101074
49.0%
N 101063
49.0%
S 668
 
0.3%
t 668
 
0.3%
e 668
 
0.3%
a 668
 
0.3%
d 668
 
0.3%
y 668
 
0.3%
U 24
 
< 0.1%
p 24
 
< 0.1%
Other values (3) 33
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 206226
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 101074
49.0%
N 101063
49.0%
S 668
 
0.3%
t 668
 
0.3%
e 668
 
0.3%
a 668
 
0.3%
d 668
 
0.3%
y 668
 
0.3%
U 24
 
< 0.1%
p 24
 
< 0.1%
Other values (3) 33
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 206226
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 101074
49.0%
N 101063
49.0%
S 668
 
0.3%
t 668
 
0.3%
e 668
 
0.3%
a 668
 
0.3%
d 668
 
0.3%
y 668
 
0.3%
U 24
 
< 0.1%
p 24
 
< 0.1%
Other values (3) 33
 
< 0.1%

chlorpropamide
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size99.9 KiB
No
101680 
Steady
 
79
Up
 
6
Down
 
1

Length

Max length6
Median length2
Mean length2.0031248
Min length2

Characters and Unicode

Total characters203850
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 101680
99.9%
Steady 79
 
0.1%
Up 6
 
< 0.1%
Down 1
 
< 0.1%

Length

2024-07-01T03:19:32.363427image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-01T03:19:32.652663image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 101680
99.9%
steady 79
 
0.1%
up 6
 
< 0.1%
down 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
o 101681
49.9%
N 101680
49.9%
S 79
 
< 0.1%
t 79
 
< 0.1%
e 79
 
< 0.1%
a 79
 
< 0.1%
d 79
 
< 0.1%
y 79
 
< 0.1%
U 6
 
< 0.1%
p 6
 
< 0.1%
Other values (3) 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 203850
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 101681
49.9%
N 101680
49.9%
S 79
 
< 0.1%
t 79
 
< 0.1%
e 79
 
< 0.1%
a 79
 
< 0.1%
d 79
 
< 0.1%
y 79
 
< 0.1%
U 6
 
< 0.1%
p 6
 
< 0.1%
Other values (3) 3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 203850
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 101681
49.9%
N 101680
49.9%
S 79
 
< 0.1%
t 79
 
< 0.1%
e 79
 
< 0.1%
a 79
 
< 0.1%
d 79
 
< 0.1%
y 79
 
< 0.1%
U 6
 
< 0.1%
p 6
 
< 0.1%
Other values (3) 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 203850
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 101681
49.9%
N 101680
49.9%
S 79
 
< 0.1%
t 79
 
< 0.1%
e 79
 
< 0.1%
a 79
 
< 0.1%
d 79
 
< 0.1%
y 79
 
< 0.1%
U 6
 
< 0.1%
p 6
 
< 0.1%
Other values (3) 3
 
< 0.1%

glimepiride
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size99.9 KiB
No
96575 
Steady
 
4670
Up
 
327
Down
 
194

Length

Max length6
Median length2
Mean length2.187371
Min length2

Characters and Unicode

Total characters222600
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 96575
94.9%
Steady 4670
 
4.6%
Up 327
 
0.3%
Down 194
 
0.2%

Length

2024-07-01T03:19:32.884445image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-01T03:19:33.170388image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 96575
94.9%
steady 4670
 
4.6%
up 327
 
0.3%
down 194
 
0.2%

Most occurring characters

ValueCountFrequency (%)
o 96769
43.5%
N 96575
43.4%
S 4670
 
2.1%
t 4670
 
2.1%
e 4670
 
2.1%
a 4670
 
2.1%
d 4670
 
2.1%
y 4670
 
2.1%
U 327
 
0.1%
p 327
 
0.1%
Other values (3) 582
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 222600
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 96769
43.5%
N 96575
43.4%
S 4670
 
2.1%
t 4670
 
2.1%
e 4670
 
2.1%
a 4670
 
2.1%
d 4670
 
2.1%
y 4670
 
2.1%
U 327
 
0.1%
p 327
 
0.1%
Other values (3) 582
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 222600
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 96769
43.5%
N 96575
43.4%
S 4670
 
2.1%
t 4670
 
2.1%
e 4670
 
2.1%
a 4670
 
2.1%
d 4670
 
2.1%
y 4670
 
2.1%
U 327
 
0.1%
p 327
 
0.1%
Other values (3) 582
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 222600
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 96769
43.5%
N 96575
43.4%
S 4670
 
2.1%
t 4670
 
2.1%
e 4670
 
2.1%
a 4670
 
2.1%
d 4670
 
2.1%
y 4670
 
2.1%
U 327
 
0.1%
p 327
 
0.1%
Other values (3) 582
 
0.3%

acetohexamide
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size99.7 KiB
No
101765 
Steady
 
1

Length

Max length6
Median length2
Mean length2.0000393
Min length2

Characters and Unicode

Total characters203536
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 101765
> 99.9%
Steady 1
 
< 0.1%

Length

2024-07-01T03:19:33.419408image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-01T03:19:33.691279image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 101765
> 99.9%
steady 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
N 101765
50.0%
o 101765
50.0%
S 1
 
< 0.1%
t 1
 
< 0.1%
e 1
 
< 0.1%
a 1
 
< 0.1%
d 1
 
< 0.1%
y 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 203536
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 101765
50.0%
o 101765
50.0%
S 1
 
< 0.1%
t 1
 
< 0.1%
e 1
 
< 0.1%
a 1
 
< 0.1%
d 1
 
< 0.1%
y 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 203536
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 101765
50.0%
o 101765
50.0%
S 1
 
< 0.1%
t 1
 
< 0.1%
e 1
 
< 0.1%
a 1
 
< 0.1%
d 1
 
< 0.1%
y 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 203536
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 101765
50.0%
o 101765
50.0%
S 1
 
< 0.1%
t 1
 
< 0.1%
e 1
 
< 0.1%
a 1
 
< 0.1%
d 1
 
< 0.1%
y 1
 
< 0.1%

glipizide
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size99.9 KiB
No
89080 
Steady
11356 
Up
 
770
Down
 
560

Length

Max length6
Median length2
Mean length2.457363
Min length2

Characters and Unicode

Total characters250076
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowSteady
4th rowNo
5th rowSteady

Common Values

ValueCountFrequency (%)
No 89080
87.5%
Steady 11356
 
11.2%
Up 770
 
0.8%
Down 560
 
0.6%

Length

2024-07-01T03:19:33.901027image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-01T03:19:34.184470image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 89080
87.5%
steady 11356
 
11.2%
up 770
 
0.8%
down 560
 
0.6%

Most occurring characters

ValueCountFrequency (%)
o 89640
35.8%
N 89080
35.6%
S 11356
 
4.5%
t 11356
 
4.5%
e 11356
 
4.5%
a 11356
 
4.5%
d 11356
 
4.5%
y 11356
 
4.5%
U 770
 
0.3%
p 770
 
0.3%
Other values (3) 1680
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 250076
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 89640
35.8%
N 89080
35.6%
S 11356
 
4.5%
t 11356
 
4.5%
e 11356
 
4.5%
a 11356
 
4.5%
d 11356
 
4.5%
y 11356
 
4.5%
U 770
 
0.3%
p 770
 
0.3%
Other values (3) 1680
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 250076
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 89640
35.8%
N 89080
35.6%
S 11356
 
4.5%
t 11356
 
4.5%
e 11356
 
4.5%
a 11356
 
4.5%
d 11356
 
4.5%
y 11356
 
4.5%
U 770
 
0.3%
p 770
 
0.3%
Other values (3) 1680
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 250076
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 89640
35.8%
N 89080
35.6%
S 11356
 
4.5%
t 11356
 
4.5%
e 11356
 
4.5%
a 11356
 
4.5%
d 11356
 
4.5%
y 11356
 
4.5%
U 770
 
0.3%
p 770
 
0.3%
Other values (3) 1680
 
0.7%

glyburide
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size99.9 KiB
No
91116 
Steady
9274 
Up
 
812
Down
 
564

Length

Max length6
Median length2
Mean length2.3756068
Min length2

Characters and Unicode

Total characters241756
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 91116
89.5%
Steady 9274
 
9.1%
Up 812
 
0.8%
Down 564
 
0.6%

Length

2024-07-01T03:19:34.442457image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-01T03:19:34.763217image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 91116
89.5%
steady 9274
 
9.1%
up 812
 
0.8%
down 564
 
0.6%

Most occurring characters

ValueCountFrequency (%)
o 91680
37.9%
N 91116
37.7%
S 9274
 
3.8%
t 9274
 
3.8%
e 9274
 
3.8%
a 9274
 
3.8%
d 9274
 
3.8%
y 9274
 
3.8%
U 812
 
0.3%
p 812
 
0.3%
Other values (3) 1692
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 241756
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 91680
37.9%
N 91116
37.7%
S 9274
 
3.8%
t 9274
 
3.8%
e 9274
 
3.8%
a 9274
 
3.8%
d 9274
 
3.8%
y 9274
 
3.8%
U 812
 
0.3%
p 812
 
0.3%
Other values (3) 1692
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 241756
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 91680
37.9%
N 91116
37.7%
S 9274
 
3.8%
t 9274
 
3.8%
e 9274
 
3.8%
a 9274
 
3.8%
d 9274
 
3.8%
y 9274
 
3.8%
U 812
 
0.3%
p 812
 
0.3%
Other values (3) 1692
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 241756
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 91680
37.9%
N 91116
37.7%
S 9274
 
3.8%
t 9274
 
3.8%
e 9274
 
3.8%
a 9274
 
3.8%
d 9274
 
3.8%
y 9274
 
3.8%
U 812
 
0.3%
p 812
 
0.3%
Other values (3) 1692
 
0.7%

tolbutamide
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size99.7 KiB
No
101743 
Steady
 
23

Length

Max length6
Median length2
Mean length2.000904
Min length2

Characters and Unicode

Total characters203624
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 101743
> 99.9%
Steady 23
 
< 0.1%

Length

2024-07-01T03:19:35.012275image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-01T03:19:35.273808image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 101743
> 99.9%
steady 23
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
N 101743
50.0%
o 101743
50.0%
S 23
 
< 0.1%
t 23
 
< 0.1%
e 23
 
< 0.1%
a 23
 
< 0.1%
d 23
 
< 0.1%
y 23
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 203624
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 101743
50.0%
o 101743
50.0%
S 23
 
< 0.1%
t 23
 
< 0.1%
e 23
 
< 0.1%
a 23
 
< 0.1%
d 23
 
< 0.1%
y 23
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 203624
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 101743
50.0%
o 101743
50.0%
S 23
 
< 0.1%
t 23
 
< 0.1%
e 23
 
< 0.1%
a 23
 
< 0.1%
d 23
 
< 0.1%
y 23
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 203624
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 101743
50.0%
o 101743
50.0%
S 23
 
< 0.1%
t 23
 
< 0.1%
e 23
 
< 0.1%
a 23
 
< 0.1%
d 23
 
< 0.1%
y 23
 
< 0.1%

pioglitazone
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size99.9 KiB
No
94438 
Steady
 
6976
Up
 
234
Down
 
118

Length

Max length6
Median length2
Mean length2.2765167
Min length2

Characters and Unicode

Total characters231672
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 94438
92.8%
Steady 6976
 
6.9%
Up 234
 
0.2%
Down 118
 
0.1%

Length

2024-07-01T03:19:35.490834image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-01T03:19:35.769490image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 94438
92.8%
steady 6976
 
6.9%
up 234
 
0.2%
down 118
 
0.1%

Most occurring characters

ValueCountFrequency (%)
o 94556
40.8%
N 94438
40.8%
S 6976
 
3.0%
t 6976
 
3.0%
e 6976
 
3.0%
a 6976
 
3.0%
d 6976
 
3.0%
y 6976
 
3.0%
U 234
 
0.1%
p 234
 
0.1%
Other values (3) 354
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 231672
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 94556
40.8%
N 94438
40.8%
S 6976
 
3.0%
t 6976
 
3.0%
e 6976
 
3.0%
a 6976
 
3.0%
d 6976
 
3.0%
y 6976
 
3.0%
U 234
 
0.1%
p 234
 
0.1%
Other values (3) 354
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 231672
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 94556
40.8%
N 94438
40.8%
S 6976
 
3.0%
t 6976
 
3.0%
e 6976
 
3.0%
a 6976
 
3.0%
d 6976
 
3.0%
y 6976
 
3.0%
U 234
 
0.1%
p 234
 
0.1%
Other values (3) 354
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 231672
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 94556
40.8%
N 94438
40.8%
S 6976
 
3.0%
t 6976
 
3.0%
e 6976
 
3.0%
a 6976
 
3.0%
d 6976
 
3.0%
y 6976
 
3.0%
U 234
 
0.1%
p 234
 
0.1%
Other values (3) 354
 
0.2%

rosiglitazone
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size99.9 KiB
No
95401 
Steady
 
6100
Up
 
178
Down
 
87

Length

Max length6
Median length2
Mean length2.2414755
Min length2

Characters and Unicode

Total characters228106
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 95401
93.7%
Steady 6100
 
6.0%
Up 178
 
0.2%
Down 87
 
0.1%

Length

2024-07-01T03:19:35.997063image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-01T03:19:36.410834image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 95401
93.7%
steady 6100
 
6.0%
up 178
 
0.2%
down 87
 
0.1%

Most occurring characters

ValueCountFrequency (%)
o 95488
41.9%
N 95401
41.8%
S 6100
 
2.7%
t 6100
 
2.7%
e 6100
 
2.7%
a 6100
 
2.7%
d 6100
 
2.7%
y 6100
 
2.7%
U 178
 
0.1%
p 178
 
0.1%
Other values (3) 261
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 228106
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 95488
41.9%
N 95401
41.8%
S 6100
 
2.7%
t 6100
 
2.7%
e 6100
 
2.7%
a 6100
 
2.7%
d 6100
 
2.7%
y 6100
 
2.7%
U 178
 
0.1%
p 178
 
0.1%
Other values (3) 261
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 228106
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 95488
41.9%
N 95401
41.8%
S 6100
 
2.7%
t 6100
 
2.7%
e 6100
 
2.7%
a 6100
 
2.7%
d 6100
 
2.7%
y 6100
 
2.7%
U 178
 
0.1%
p 178
 
0.1%
Other values (3) 261
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 228106
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 95488
41.9%
N 95401
41.8%
S 6100
 
2.7%
t 6100
 
2.7%
e 6100
 
2.7%
a 6100
 
2.7%
d 6100
 
2.7%
y 6100
 
2.7%
U 178
 
0.1%
p 178
 
0.1%
Other values (3) 261
 
0.1%

acarbose
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size99.9 KiB
No
101458 
Steady
 
295
Up
 
10
Down
 
3

Length

Max length6
Median length2
Mean length2.0116542
Min length2

Characters and Unicode

Total characters204718
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 101458
99.7%
Steady 295
 
0.3%
Up 10
 
< 0.1%
Down 3
 
< 0.1%

Length

2024-07-01T03:19:36.773339image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-01T03:19:37.213733image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 101458
99.7%
steady 295
 
0.3%
up 10
 
< 0.1%
down 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
o 101461
49.6%
N 101458
49.6%
S 295
 
0.1%
t 295
 
0.1%
e 295
 
0.1%
a 295
 
0.1%
d 295
 
0.1%
y 295
 
0.1%
U 10
 
< 0.1%
p 10
 
< 0.1%
Other values (3) 9
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 204718
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 101461
49.6%
N 101458
49.6%
S 295
 
0.1%
t 295
 
0.1%
e 295
 
0.1%
a 295
 
0.1%
d 295
 
0.1%
y 295
 
0.1%
U 10
 
< 0.1%
p 10
 
< 0.1%
Other values (3) 9
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 204718
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 101461
49.6%
N 101458
49.6%
S 295
 
0.1%
t 295
 
0.1%
e 295
 
0.1%
a 295
 
0.1%
d 295
 
0.1%
y 295
 
0.1%
U 10
 
< 0.1%
p 10
 
< 0.1%
Other values (3) 9
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 204718
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 101461
49.6%
N 101458
49.6%
S 295
 
0.1%
t 295
 
0.1%
e 295
 
0.1%
a 295
 
0.1%
d 295
 
0.1%
y 295
 
0.1%
U 10
 
< 0.1%
p 10
 
< 0.1%
Other values (3) 9
 
< 0.1%

miglitol
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size99.9 KiB
No
101728 
Steady
 
31
Down
 
5
Up
 
2

Length

Max length6
Median length2
Mean length2.0013167
Min length2

Characters and Unicode

Total characters203666
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 101728
> 99.9%
Steady 31
 
< 0.1%
Down 5
 
< 0.1%
Up 2
 
< 0.1%

Length

2024-07-01T03:19:37.655958image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-01T03:19:38.107977image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 101728
> 99.9%
steady 31
 
< 0.1%
down 5
 
< 0.1%
up 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
o 101733
50.0%
N 101728
49.9%
S 31
 
< 0.1%
t 31
 
< 0.1%
e 31
 
< 0.1%
a 31
 
< 0.1%
d 31
 
< 0.1%
y 31
 
< 0.1%
D 5
 
< 0.1%
w 5
 
< 0.1%
Other values (3) 9
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 203666
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 101733
50.0%
N 101728
49.9%
S 31
 
< 0.1%
t 31
 
< 0.1%
e 31
 
< 0.1%
a 31
 
< 0.1%
d 31
 
< 0.1%
y 31
 
< 0.1%
D 5
 
< 0.1%
w 5
 
< 0.1%
Other values (3) 9
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 203666
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 101733
50.0%
N 101728
49.9%
S 31
 
< 0.1%
t 31
 
< 0.1%
e 31
 
< 0.1%
a 31
 
< 0.1%
d 31
 
< 0.1%
y 31
 
< 0.1%
D 5
 
< 0.1%
w 5
 
< 0.1%
Other values (3) 9
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 203666
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 101733
50.0%
N 101728
49.9%
S 31
 
< 0.1%
t 31
 
< 0.1%
e 31
 
< 0.1%
a 31
 
< 0.1%
d 31
 
< 0.1%
y 31
 
< 0.1%
D 5
 
< 0.1%
w 5
 
< 0.1%
Other values (3) 9
 
< 0.1%

troglitazone
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size99.7 KiB
No
101763 
Steady
 
3

Length

Max length6
Median length2
Mean length2.0001179
Min length2

Characters and Unicode

Total characters203544
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 101763
> 99.9%
Steady 3
 
< 0.1%

Length

2024-07-01T03:19:38.544092image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-01T03:19:39.038111image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 101763
> 99.9%
steady 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
N 101763
50.0%
o 101763
50.0%
S 3
 
< 0.1%
t 3
 
< 0.1%
e 3
 
< 0.1%
a 3
 
< 0.1%
d 3
 
< 0.1%
y 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 203544
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 101763
50.0%
o 101763
50.0%
S 3
 
< 0.1%
t 3
 
< 0.1%
e 3
 
< 0.1%
a 3
 
< 0.1%
d 3
 
< 0.1%
y 3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 203544
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 101763
50.0%
o 101763
50.0%
S 3
 
< 0.1%
t 3
 
< 0.1%
e 3
 
< 0.1%
a 3
 
< 0.1%
d 3
 
< 0.1%
y 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 203544
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 101763
50.0%
o 101763
50.0%
S 3
 
< 0.1%
t 3
 
< 0.1%
e 3
 
< 0.1%
a 3
 
< 0.1%
d 3
 
< 0.1%
y 3
 
< 0.1%

tolazamide
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size99.8 KiB
No
101727 
Steady
 
38
Up
 
1

Length

Max length6
Median length2
Mean length2.0014936
Min length2

Characters and Unicode

Total characters203684
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 101727
> 99.9%
Steady 38
 
< 0.1%
Up 1
 
< 0.1%

Length

2024-07-01T03:19:39.464563image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-01T03:19:39.827746image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 101727
> 99.9%
steady 38
 
< 0.1%
up 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
N 101727
49.9%
o 101727
49.9%
S 38
 
< 0.1%
t 38
 
< 0.1%
e 38
 
< 0.1%
a 38
 
< 0.1%
d 38
 
< 0.1%
y 38
 
< 0.1%
U 1
 
< 0.1%
p 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 203684
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 101727
49.9%
o 101727
49.9%
S 38
 
< 0.1%
t 38
 
< 0.1%
e 38
 
< 0.1%
a 38
 
< 0.1%
d 38
 
< 0.1%
y 38
 
< 0.1%
U 1
 
< 0.1%
p 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 203684
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 101727
49.9%
o 101727
49.9%
S 38
 
< 0.1%
t 38
 
< 0.1%
e 38
 
< 0.1%
a 38
 
< 0.1%
d 38
 
< 0.1%
y 38
 
< 0.1%
U 1
 
< 0.1%
p 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 203684
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 101727
49.9%
o 101727
49.9%
S 38
 
< 0.1%
t 38
 
< 0.1%
e 38
 
< 0.1%
a 38
 
< 0.1%
d 38
 
< 0.1%
y 38
 
< 0.1%
U 1
 
< 0.1%
p 1
 
< 0.1%

examide
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size99.7 KiB
No
101766 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters203532
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 101766
100.0%

Length

2024-07-01T03:19:40.048593image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-01T03:19:40.276909image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 101766
100.0%

Most occurring characters

ValueCountFrequency (%)
N 101766
50.0%
o 101766
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 203532
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 101766
50.0%
o 101766
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 203532
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 101766
50.0%
o 101766
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 203532
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 101766
50.0%
o 101766
50.0%

citoglipton
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size99.7 KiB
No
101766 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters203532
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 101766
100.0%

Length

2024-07-01T03:19:40.463889image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-01T03:19:40.688310image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 101766
100.0%

Most occurring characters

ValueCountFrequency (%)
N 101766
50.0%
o 101766
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 203532
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 101766
50.0%
o 101766
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 203532
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 101766
50.0%
o 101766
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 203532
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 101766
50.0%
o 101766
50.0%

insulin
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size99.9 KiB
No
47383 
Steady
30849 
Down
12218 
Up
11316 

Length

Max length6
Median length2
Mean length3.4526659
Min length2

Characters and Unicode

Total characters351364
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowUp
3rd rowNo
4th rowUp
5th rowSteady

Common Values

ValueCountFrequency (%)
No 47383
46.6%
Steady 30849
30.3%
Down 12218
 
12.0%
Up 11316
 
11.1%

Length

2024-07-01T03:19:40.896498image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-01T03:19:41.217088image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 47383
46.6%
steady 30849
30.3%
down 12218
 
12.0%
up 11316
 
11.1%

Most occurring characters

ValueCountFrequency (%)
o 59601
17.0%
N 47383
13.5%
S 30849
8.8%
t 30849
8.8%
e 30849
8.8%
a 30849
8.8%
d 30849
8.8%
y 30849
8.8%
D 12218
 
3.5%
w 12218
 
3.5%
Other values (3) 34850
9.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 351364
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 59601
17.0%
N 47383
13.5%
S 30849
8.8%
t 30849
8.8%
e 30849
8.8%
a 30849
8.8%
d 30849
8.8%
y 30849
8.8%
D 12218
 
3.5%
w 12218
 
3.5%
Other values (3) 34850
9.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 351364
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 59601
17.0%
N 47383
13.5%
S 30849
8.8%
t 30849
8.8%
e 30849
8.8%
a 30849
8.8%
d 30849
8.8%
y 30849
8.8%
D 12218
 
3.5%
w 12218
 
3.5%
Other values (3) 34850
9.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 351364
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 59601
17.0%
N 47383
13.5%
S 30849
8.8%
t 30849
8.8%
e 30849
8.8%
a 30849
8.8%
d 30849
8.8%
y 30849
8.8%
D 12218
 
3.5%
w 12218
 
3.5%
Other values (3) 34850
9.9%

glyburide-metformin
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size99.9 KiB
No
101060 
Steady
 
692
Up
 
8
Down
 
6

Length

Max length6
Median length2
Mean length2.0273176
Min length2

Characters and Unicode

Total characters206312
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 101060
99.3%
Steady 692
 
0.7%
Up 8
 
< 0.1%
Down 6
 
< 0.1%

Length

2024-07-01T03:19:41.475389image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-01T03:19:41.759611image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 101060
99.3%
steady 692
 
0.7%
up 8
 
< 0.1%
down 6
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
o 101066
49.0%
N 101060
49.0%
S 692
 
0.3%
t 692
 
0.3%
e 692
 
0.3%
a 692
 
0.3%
d 692
 
0.3%
y 692
 
0.3%
U 8
 
< 0.1%
p 8
 
< 0.1%
Other values (3) 18
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 206312
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 101066
49.0%
N 101060
49.0%
S 692
 
0.3%
t 692
 
0.3%
e 692
 
0.3%
a 692
 
0.3%
d 692
 
0.3%
y 692
 
0.3%
U 8
 
< 0.1%
p 8
 
< 0.1%
Other values (3) 18
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 206312
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 101066
49.0%
N 101060
49.0%
S 692
 
0.3%
t 692
 
0.3%
e 692
 
0.3%
a 692
 
0.3%
d 692
 
0.3%
y 692
 
0.3%
U 8
 
< 0.1%
p 8
 
< 0.1%
Other values (3) 18
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 206312
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 101066
49.0%
N 101060
49.0%
S 692
 
0.3%
t 692
 
0.3%
e 692
 
0.3%
a 692
 
0.3%
d 692
 
0.3%
y 692
 
0.3%
U 8
 
< 0.1%
p 8
 
< 0.1%
Other values (3) 18
 
< 0.1%

glipizide-metformin
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size99.7 KiB
No
101753 
Steady
 
13

Length

Max length6
Median length2
Mean length2.000511
Min length2

Characters and Unicode

Total characters203584
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 101753
> 99.9%
Steady 13
 
< 0.1%

Length

2024-07-01T03:19:42.001848image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-01T03:19:42.295466image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 101753
> 99.9%
steady 13
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
N 101753
50.0%
o 101753
50.0%
S 13
 
< 0.1%
t 13
 
< 0.1%
e 13
 
< 0.1%
a 13
 
< 0.1%
d 13
 
< 0.1%
y 13
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 203584
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 101753
50.0%
o 101753
50.0%
S 13
 
< 0.1%
t 13
 
< 0.1%
e 13
 
< 0.1%
a 13
 
< 0.1%
d 13
 
< 0.1%
y 13
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 203584
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 101753
50.0%
o 101753
50.0%
S 13
 
< 0.1%
t 13
 
< 0.1%
e 13
 
< 0.1%
a 13
 
< 0.1%
d 13
 
< 0.1%
y 13
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 203584
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 101753
50.0%
o 101753
50.0%
S 13
 
< 0.1%
t 13
 
< 0.1%
e 13
 
< 0.1%
a 13
 
< 0.1%
d 13
 
< 0.1%
y 13
 
< 0.1%

glimepiride-pioglitazone
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size99.7 KiB
No
101765 
Steady
 
1

Length

Max length6
Median length2
Mean length2.0000393
Min length2

Characters and Unicode

Total characters203536
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 101765
> 99.9%
Steady 1
 
< 0.1%

Length

2024-07-01T03:19:42.512455image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-01T03:19:42.776663image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 101765
> 99.9%
steady 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
N 101765
50.0%
o 101765
50.0%
S 1
 
< 0.1%
t 1
 
< 0.1%
e 1
 
< 0.1%
a 1
 
< 0.1%
d 1
 
< 0.1%
y 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 203536
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 101765
50.0%
o 101765
50.0%
S 1
 
< 0.1%
t 1
 
< 0.1%
e 1
 
< 0.1%
a 1
 
< 0.1%
d 1
 
< 0.1%
y 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 203536
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 101765
50.0%
o 101765
50.0%
S 1
 
< 0.1%
t 1
 
< 0.1%
e 1
 
< 0.1%
a 1
 
< 0.1%
d 1
 
< 0.1%
y 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 203536
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 101765
50.0%
o 101765
50.0%
S 1
 
< 0.1%
t 1
 
< 0.1%
e 1
 
< 0.1%
a 1
 
< 0.1%
d 1
 
< 0.1%
y 1
 
< 0.1%

metformin-rosiglitazone
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size99.7 KiB
No
101764 
Steady
 
2

Length

Max length6
Median length2
Mean length2.0000786
Min length2

Characters and Unicode

Total characters203540
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 101764
> 99.9%
Steady 2
 
< 0.1%

Length

2024-07-01T03:19:42.997192image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-01T03:19:43.299041image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 101764
> 99.9%
steady 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
N 101764
50.0%
o 101764
50.0%
S 2
 
< 0.1%
t 2
 
< 0.1%
e 2
 
< 0.1%
a 2
 
< 0.1%
d 2
 
< 0.1%
y 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 203540
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 101764
50.0%
o 101764
50.0%
S 2
 
< 0.1%
t 2
 
< 0.1%
e 2
 
< 0.1%
a 2
 
< 0.1%
d 2
 
< 0.1%
y 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 203540
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 101764
50.0%
o 101764
50.0%
S 2
 
< 0.1%
t 2
 
< 0.1%
e 2
 
< 0.1%
a 2
 
< 0.1%
d 2
 
< 0.1%
y 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 203540
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 101764
50.0%
o 101764
50.0%
S 2
 
< 0.1%
t 2
 
< 0.1%
e 2
 
< 0.1%
a 2
 
< 0.1%
d 2
 
< 0.1%
y 2
 
< 0.1%

metformin-pioglitazone
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size99.7 KiB
No
101765 
Steady
 
1

Length

Max length6
Median length2
Mean length2.0000393
Min length2

Characters and Unicode

Total characters203536
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 101765
> 99.9%
Steady 1
 
< 0.1%

Length

2024-07-01T03:19:43.516636image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-01T03:19:44.331170image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 101765
> 99.9%
steady 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
N 101765
50.0%
o 101765
50.0%
S 1
 
< 0.1%
t 1
 
< 0.1%
e 1
 
< 0.1%
a 1
 
< 0.1%
d 1
 
< 0.1%
y 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 203536
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 101765
50.0%
o 101765
50.0%
S 1
 
< 0.1%
t 1
 
< 0.1%
e 1
 
< 0.1%
a 1
 
< 0.1%
d 1
 
< 0.1%
y 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 203536
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 101765
50.0%
o 101765
50.0%
S 1
 
< 0.1%
t 1
 
< 0.1%
e 1
 
< 0.1%
a 1
 
< 0.1%
d 1
 
< 0.1%
y 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 203536
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 101765
50.0%
o 101765
50.0%
S 1
 
< 0.1%
t 1
 
< 0.1%
e 1
 
< 0.1%
a 1
 
< 0.1%
d 1
 
< 0.1%
y 1
 
< 0.1%

change
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size99.7 KiB
No
54755 
Ch
47011 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters203532
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowCh
3rd rowNo
4th rowCh
5th rowCh

Common Values

ValueCountFrequency (%)
No 54755
53.8%
Ch 47011
46.2%

Length

2024-07-01T03:19:44.539713image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-01T03:19:44.787450image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 54755
53.8%
ch 47011
46.2%

Most occurring characters

ValueCountFrequency (%)
N 54755
26.9%
o 54755
26.9%
C 47011
23.1%
h 47011
23.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 203532
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 54755
26.9%
o 54755
26.9%
C 47011
23.1%
h 47011
23.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 203532
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 54755
26.9%
o 54755
26.9%
C 47011
23.1%
h 47011
23.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 203532
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 54755
26.9%
o 54755
26.9%
C 47011
23.1%
h 47011
23.1%

diabetesMed
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size99.7 KiB
Yes
78363 
No
23403 

Length

Max length3
Median length3
Mean length2.7700312
Min length2

Characters and Unicode

Total characters281895
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowYes
3rd rowYes
4th rowYes
5th rowYes

Common Values

ValueCountFrequency (%)
Yes 78363
77.0%
No 23403
 
23.0%

Length

2024-07-01T03:19:44.997760image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-01T03:19:45.241693image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
yes 78363
77.0%
no 23403
 
23.0%

Most occurring characters

ValueCountFrequency (%)
Y 78363
27.8%
e 78363
27.8%
s 78363
27.8%
N 23403
 
8.3%
o 23403
 
8.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 281895
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
Y 78363
27.8%
e 78363
27.8%
s 78363
27.8%
N 23403
 
8.3%
o 23403
 
8.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 281895
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
Y 78363
27.8%
e 78363
27.8%
s 78363
27.8%
N 23403
 
8.3%
o 23403
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 281895
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
Y 78363
27.8%
e 78363
27.8%
s 78363
27.8%
N 23403
 
8.3%
o 23403
 
8.3%

readmitted
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size99.8 KiB
NO
54864 
>30
35545 
<30
11357 

Length

Max length3
Median length2
Mean length2.4608808
Min length2

Characters and Unicode

Total characters250434
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNO
2nd row>30
3rd rowNO
4th rowNO
5th rowNO

Common Values

ValueCountFrequency (%)
NO 54864
53.9%
>30 35545
34.9%
<30 11357
 
11.2%

Length

2024-07-01T03:19:45.459887image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-01T03:19:45.712343image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 54864
53.9%
30 46902
46.1%

Most occurring characters

ValueCountFrequency (%)
N 54864
21.9%
O 54864
21.9%
3 46902
18.7%
0 46902
18.7%
> 35545
14.2%
< 11357
 
4.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 250434
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 54864
21.9%
O 54864
21.9%
3 46902
18.7%
0 46902
18.7%
> 35545
14.2%
< 11357
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 250434
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 54864
21.9%
O 54864
21.9%
3 46902
18.7%
0 46902
18.7%
> 35545
14.2%
< 11357
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 250434
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 54864
21.9%
O 54864
21.9%
3 46902
18.7%
0 46902
18.7%
> 35545
14.2%
< 11357
 
4.5%

Interactions

2024-07-01T03:19:09.390263image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:21.556342image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:25.196963image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:29.415824image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:33.719557image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:37.237642image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:40.962965image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:45.745730image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:49.329967image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:53.259566image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:57.915363image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:19:01.648365image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:19:05.270195image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:19:09.724938image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:21.812260image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:25.454278image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:29.738199image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:33.975948image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:37.496906image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:41.274623image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:46.002249image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:49.589465image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:53.527374image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:58.298014image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:19:01.914294image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:19:05.528050image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:19:10.045827image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:22.085226image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:25.734349image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:30.109180image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:34.262335image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:37.790201image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:41.667946image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:46.276340image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:49.896924image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:53.816025image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:58.688605image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:19:02.199738image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:19:05.812513image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:19:10.462602image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:22.350640image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:26.022086image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:30.551805image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:34.528879image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:38.057818image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:42.042756image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:46.555545image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:50.182842image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:54.125290image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:58.952487image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:19:02.488325image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:19:06.099724image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:19:10.807832image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:22.611856image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:26.280580image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:30.960319image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:34.797219image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:38.314248image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:42.396599image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:46.858023image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:50.458728image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:54.390970image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:59.216541image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:19:02.766155image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:19:06.744660image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:19:11.225508image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:22.880309image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:26.543519image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:31.291184image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:35.052525image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:38.570396image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:42.822811image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:47.119025image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:50.736512image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:54.675442image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:59.487622image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:19:03.028878image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:19:06.987299image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:19:11.676346image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:23.146054image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:26.850962image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:31.564706image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:35.323562image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:38.845307image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:43.211903image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:47.383528image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:51.024415image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:54.983654image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:59.757504image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:19:03.317722image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:19:07.256227image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:19:12.125409image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:23.405694image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:27.125039image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:31.840112image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:35.593508image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:39.113146image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:43.651932image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:47.672970image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:51.303007image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:55.403400image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:19:00.015053image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:19:03.611119image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:19:07.517177image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:19:12.542813image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:23.665025image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:27.404023image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:32.338054image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:35.862310image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:39.380096image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:44.047636image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:47.946572image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:51.570257image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:55.829707image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:19:00.288874image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:19:03.887711image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:19:07.820467image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:19:12.861950image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:23.963218image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:27.820227image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:32.625525image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:36.152607image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:39.664326image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:44.482341image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:48.230974image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:51.852054image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:56.226693image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:19:00.560888image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:19:04.169263image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:19:08.092925image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:19:13.118898image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:24.215094image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:28.203566image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:32.893704image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:36.417773image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:39.908518image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:44.901701image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:48.483876image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:52.117438image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:56.643187image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:19:00.820005image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:19:04.423067image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:19:08.350661image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:19:13.397904image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:24.665169image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:28.606434image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:33.181543image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:36.709968image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:40.186808image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:45.193869image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:48.778749image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:52.718583image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:57.065689image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:19:01.089890image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:19:04.725140image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:19:08.663146image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:19:13.685298image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:24.932157image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:29.009687image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:33.431824image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:36.966493image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:40.440495image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:45.449319image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:49.054506image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:52.976076image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:18:57.465934image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:19:01.367284image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:19:04.985066image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-01T03:19:09.004449image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2024-07-01T03:19:46.012416image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A1Cresultacarboseacetohexamideadmission_source_idadmission_type_idagechangechlorpropamidediabetesMeddischarge_disposition_idencounter_idgenderglimepirideglimepiride-pioglitazoneglipizideglipizide-metforminglyburideglyburide-metformininsulinmax_glu_serummedical_specialtymetforminmetformin-pioglitazonemetformin-rosiglitazonemiglitolnateglinidenum_lab_proceduresnum_medicationsnum_proceduresnumber_diagnosesnumber_emergencynumber_inpatientnumber_outpatientpatient_nbrpayer_codepioglitazoneracereadmittedrepagliniderosiglitazonetime_in_hospitaltolazamidetolbutamidetroglitazoneweight
A1Cresult1.0000.0120.000-0.0150.0150.1060.1150.0000.0960.0220.0390.0160.0200.0000.0230.0060.0170.0000.0800.0430.0990.0420.0000.0040.0000.001-0.107-0.0020.0150.0280.0120.0330.0160.0400.0670.0110.0320.0180.0200.010-0.0210.0100.0000.0000.017
acarbose0.0121.0000.0000.0010.0060.0020.0460.0000.0300.0040.0010.0070.0100.0000.0220.0000.0070.0040.0110.0020.0000.0130.0000.0000.0010.000-0.0010.019-0.0020.006-0.0010.0010.0180.0100.0100.0070.0070.0120.0120.0020.0050.0000.0000.0000.000
acetohexamide0.0000.0001.0000.002-0.0030.0000.0000.0000.0000.006-0.0030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0040.0050.0050.003-0.001-0.002-0.001-0.0030.0000.0000.0000.0000.0000.0000.0050.0000.0000.0000.000
admission_source_id-0.0150.0010.0021.000-0.3830.0350.0230.0000.0180.042-0.0510.0120.0160.0000.0070.0000.0190.0170.0410.3630.1810.0290.0000.0000.0000.0070.136-0.063-0.2050.1060.1040.0560.0240.0300.0810.0170.0740.0560.0180.0210.0030.0000.0040.0000.031
admission_type_id0.0150.006-0.003-0.3831.0000.0380.0630.0020.0430.021-0.1230.0130.0360.0000.0120.0000.0070.0270.0640.3410.2280.0320.0000.0000.0050.012-0.2240.0870.217-0.127-0.033-0.0450.0300.0070.1350.0200.0630.0440.0340.019-0.0150.0060.0130.0000.043
age0.1060.0020.0000.0350.0381.0000.0560.0030.0440.2530.0710.0780.0240.0000.0370.0000.0500.0100.0680.0360.2520.0660.0000.0000.0040.0080.0280.027-0.0580.196-0.0570.0150.0230.0750.1530.0300.0850.0380.0290.0260.1200.0000.0140.0000.026
change0.1150.0460.0000.0230.0630.0561.0000.0120.506-0.011-0.1110.0140.1440.0000.2090.0070.1910.0430.6410.0570.1040.3290.0000.0000.0140.055-0.066-0.2540.004-0.056-0.054-0.026-0.031-0.0650.1480.2030.0210.0460.0780.196-0.1180.0000.0000.0030.048
chlorpropamide0.0000.0000.0000.0000.0020.0030.0121.0000.0150.011-0.0220.0000.0000.0000.0020.0000.0000.0000.0100.0000.0140.0030.0000.0000.0000.000-0.0010.0000.005-0.011-0.009-0.007-0.004-0.0130.0030.0000.0030.0040.0000.0000.0050.0000.0000.0000.000
diabetesMed0.0960.0300.0000.0180.0430.0440.5060.0151.000-0.0150.0580.0150.1270.0000.2060.0040.1870.0450.5850.0500.0670.2700.0000.0000.0090.0450.0370.199-0.0170.0250.0370.0330.0200.0220.0950.1520.0220.0610.0680.1410.0720.0100.0070.0000.036
discharge_disposition_id0.0220.0040.0060.0420.0210.253-0.0110.011-0.0151.000-0.0650.0270.0220.0000.0280.0100.0510.0150.0780.0740.0920.0360.0000.0000.0040.0060.0590.1710.0130.1510.0070.0850.033-0.0460.0940.0240.0280.1200.0160.0170.2760.0170.0100.0110.016
encounter_id0.0390.001-0.003-0.051-0.1230.071-0.111-0.0220.058-0.0651.0000.0110.0300.0010.0230.0040.0540.0290.1020.1250.1580.0280.0140.0110.0050.022-0.0090.102-0.0310.2930.1310.0370.1510.5440.2440.0360.0780.0730.0190.044-0.0600.0140.0100.0130.020
gender0.0160.0070.0000.0120.0130.0780.0140.0000.0150.0270.0111.0000.0000.0000.0190.0050.0230.0000.0000.0000.0790.0000.0000.0020.0040.000-0.008-0.0390.049-0.002-0.031-0.014-0.0130.0080.0610.0040.0540.0130.0000.011-0.0390.0030.0000.0040.027
glimepiride0.0200.0100.0000.0160.0360.0240.1440.0000.1270.0220.0300.0001.0000.0000.0420.0000.0400.0040.0100.0190.0440.0280.0000.0000.0130.009-0.0040.0390.0060.0100.000-0.011-0.0130.0270.0330.0260.0140.0070.0030.0250.0100.0000.0000.0050.007
glimepiride-pioglitazone0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0010.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000-0.001-0.004-0.003-0.005-0.0010.005-0.001-0.0010.0050.0000.0000.0000.0000.000-0.0030.0000.0000.0000.000
glipizide0.0230.0220.0000.0070.0120.0370.2090.0020.2060.0280.0230.0190.0420.0001.0000.0000.0620.0150.0340.0110.0320.0490.0000.0000.0140.0070.0030.049-0.003-0.011-0.004-0.0170.0070.0080.0170.0290.0140.0150.0100.0270.0070.0000.0020.0000.013
glipizide-metformin0.0060.0000.0000.0000.0000.0000.0070.0000.0040.0100.0040.0050.0000.0000.0001.0000.0000.0300.0000.0000.0000.0000.0000.0000.0000.000-0.0060.007-0.006-0.004-0.0040.0010.010-0.0020.0270.0000.0000.0010.0000.000-0.0010.0000.0000.0000.000
glyburide0.0170.0070.0000.0190.0070.0500.1910.0000.1870.0510.0540.0230.0400.0000.0620.0001.0000.0040.0540.0060.0390.0930.0000.0000.0000.011-0.0110.0280.000-0.033-0.031-0.028-0.009-0.0490.0400.0160.0170.0040.0140.0250.0130.0000.0000.0000.004
glyburide-metformin0.0000.0040.0000.0170.0270.0100.0430.0000.0450.0150.0290.0000.0040.0000.0150.0300.0041.0000.0050.0090.0300.0120.0000.0000.0000.004-0.0080.009-0.001-0.010-0.004-0.010-0.0050.0350.0390.0180.0180.0040.0030.002-0.0030.0000.0000.0000.000
insulin0.0800.0110.0000.0410.0640.0680.6410.0100.5850.0780.1020.0000.0100.0000.0340.0000.0540.0051.0000.0470.1080.0320.0000.0030.0040.0040.0350.0730.0020.0320.0160.0090.003-0.0070.1310.0090.0420.0500.0180.0130.0460.0080.0000.0000.055
max_glu_serum0.0430.0020.0000.3630.3410.0360.0570.0000.0500.0740.1250.0000.0190.0000.0110.0000.0060.0090.0471.0000.1060.0190.0000.0000.0000.009-0.027-0.0410.001-0.009-0.014-0.0120.002-0.0140.0800.0110.0400.0150.0100.003-0.0400.0000.0130.0000.023
medical_specialty0.0990.0000.0000.1810.2280.2520.1040.0140.0670.0920.1580.0790.0440.0000.0320.0000.0390.0300.1080.1061.0000.0650.0130.0000.0360.000-0.1030.1060.072-0.021-0.027-0.0110.066-0.0000.1080.0230.0940.0770.0630.0380.0350.0000.0000.0000.051
metformin0.0420.0130.0000.0290.0320.0660.3290.0030.2700.0360.0280.0000.0280.0000.0490.0000.0930.0120.0320.0190.0651.0000.0410.0000.0080.013-0.0440.058-0.036-0.071-0.015-0.067-0.0040.0150.0440.0340.0120.0220.0090.061-0.0050.0080.0050.0000.014
metformin-pioglitazone0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0140.0000.0000.0000.0000.0000.0000.0000.0000.0000.0130.0411.0000.0000.0000.000-0.0040.0030.001-0.005-0.001-0.002-0.001-0.0010.0150.0100.0000.0000.0000.0000.0030.0000.0000.0000.000
metformin-rosiglitazone0.0040.0000.0000.0000.0000.0000.0000.0000.0000.0000.0110.0020.0000.0000.0000.0000.0000.0000.0030.0000.0000.0000.0001.0000.0000.0000.0010.0020.0020.004-0.0020.001-0.0020.0060.0150.0000.0280.0000.0000.000-0.0000.0000.0000.0000.000
miglitol0.0000.0010.0000.0000.0050.0040.0140.0000.0090.0040.0050.0040.0130.0000.0140.0000.0000.0000.0040.0000.0360.0080.0000.0001.0000.005-0.0030.0030.001-0.0010.004-0.004-0.0070.0080.0000.0000.0000.0050.0080.0000.0040.0000.0000.0000.000
nateglinide0.0010.0000.0000.0070.0120.0080.0550.0000.0450.0060.0220.0000.0090.0000.0070.0000.0110.0040.0040.0090.0000.0130.0000.0000.0051.000-0.0060.023-0.0020.0140.005-0.0030.0110.0210.0130.0200.0100.0000.0000.0090.0050.0000.0000.0000.004
num_lab_procedures-0.107-0.0010.0040.136-0.2240.028-0.066-0.0010.0370.059-0.009-0.008-0.004-0.0010.003-0.006-0.011-0.0080.035-0.027-0.103-0.044-0.0040.001-0.003-0.0061.0000.2520.0230.1690.0060.041-0.0240.0270.0470.0180.0410.0320.0200.0110.3370.0000.0060.0000.038
num_medications-0.0020.0190.005-0.0630.0870.027-0.2540.0000.1990.1710.102-0.0390.039-0.0040.0490.0070.0280.0090.073-0.0410.1060.0580.0030.0020.0030.0230.2521.0000.3520.2940.0440.0990.0740.0450.0380.0430.0300.0630.0160.0320.4650.0000.0000.0000.008
num_procedures0.015-0.0020.005-0.2050.217-0.0580.0040.005-0.0170.013-0.0310.0490.006-0.003-0.003-0.0060.000-0.0010.0020.0010.072-0.0360.0010.0020.001-0.0020.0230.3521.0000.067-0.046-0.064-0.024-0.0190.0430.0100.0250.0370.0000.0080.1870.0070.0000.0000.011
number_diagnoses0.0280.0060.0030.106-0.1270.196-0.056-0.0110.0250.1510.293-0.0020.010-0.005-0.011-0.004-0.033-0.0100.032-0.009-0.021-0.071-0.0050.004-0.0010.0140.1690.2940.0671.0000.0920.1360.1130.2400.0790.0100.0630.0820.0220.0080.2370.0090.0000.0000.022
number_emergency0.012-0.001-0.0010.104-0.033-0.057-0.054-0.0090.0370.0070.131-0.0310.000-0.001-0.004-0.004-0.031-0.0040.016-0.014-0.027-0.015-0.001-0.0020.0040.0050.0060.044-0.0460.0921.0000.2220.1770.1130.0340.0000.0040.0290.0000.000-0.0010.0000.0000.0000.000
number_inpatient0.0330.001-0.0020.056-0.0450.015-0.026-0.0070.0330.0850.037-0.014-0.0110.005-0.0170.001-0.028-0.0100.009-0.012-0.011-0.067-0.0020.001-0.004-0.0030.0410.099-0.0640.1360.2221.0000.1560.0260.0290.0110.0140.1300.0000.0080.0920.0000.0000.0000.014
number_outpatient0.0160.018-0.0010.0240.0300.023-0.031-0.0040.0200.0330.151-0.013-0.013-0.0010.0070.010-0.009-0.0050.0030.0020.066-0.004-0.001-0.002-0.0070.011-0.0240.074-0.0240.1130.1770.1561.0000.1550.0240.0000.0120.0280.0000.000-0.0130.0000.0000.0000.019
patient_nbr0.0400.010-0.0030.0300.0070.075-0.065-0.0130.022-0.0460.5440.0080.027-0.0010.008-0.002-0.0490.035-0.007-0.014-0.0000.015-0.0010.0060.0080.0210.0270.045-0.0190.2400.1130.0260.1551.0000.1740.0320.1060.1150.0420.019-0.0170.0090.0000.0000.037
payer_code0.0670.0100.0000.0810.1350.1530.1480.0030.0950.0940.2440.0610.0330.0050.0170.0270.0400.0390.1310.0800.1080.0440.0150.0150.0000.0130.0470.0380.0430.0790.0340.0290.0240.1741.0000.0310.0870.0490.0250.0150.0250.0000.0000.0000.054
pioglitazone0.0110.0070.0000.0170.0200.0300.2030.0000.1520.0240.0360.0040.0260.0000.0290.0000.0160.0180.0090.0110.0230.0340.0100.0000.0000.0200.0180.0430.0100.0100.0000.0110.0000.0320.0311.0000.0150.0110.0150.0370.0020.0000.0000.0000.019
race0.0320.0070.0000.0740.0630.0850.0210.0030.0220.0280.0780.0540.0140.0000.0140.0000.0170.0180.0420.0400.0940.0120.0000.0280.0000.0100.0410.0300.0250.0630.0040.0140.0120.1060.0870.0151.0000.0370.0160.006-0.0190.0000.0000.0000.036
readmitted0.0180.0120.0000.0560.0440.0380.0460.0040.0610.1200.0730.0130.0070.0000.0150.0010.0040.0040.0500.0150.0770.0220.0000.0000.0050.0000.0320.0630.0370.0820.0290.1300.0280.1150.0490.0110.0371.0000.0160.013-0.0670.0020.0000.0000.035
repaglinide0.0200.0120.0000.0180.0340.0290.0780.0000.0680.0160.0190.0000.0030.0000.0100.0000.0140.0030.0180.0100.0630.0090.0000.0000.0080.0000.0200.0160.0000.0220.0000.0000.0000.0420.0250.0150.0160.0161.0000.0060.0310.0000.0000.0000.000
rosiglitazone0.0100.0020.0000.0210.0190.0260.1960.0000.1410.0170.0440.0110.0250.0000.0270.0000.0250.0020.0130.0030.0380.0610.0000.0000.0000.0090.0110.0320.0080.0080.0000.0080.0000.0190.0150.0370.0060.0130.0061.0000.0060.0000.0000.0030.004
time_in_hospital-0.0210.0050.0050.003-0.0150.120-0.1180.0050.0720.276-0.060-0.0390.010-0.0030.007-0.0010.013-0.0030.046-0.0400.035-0.0050.003-0.0000.0040.0050.3370.4650.1870.237-0.0010.092-0.013-0.0170.0250.002-0.019-0.0670.0310.0061.0000.0000.0000.0130.010
tolazamide0.0100.0000.0000.0000.0060.0000.0000.0000.0100.0170.0140.0030.0000.0000.0000.0000.0000.0000.0080.0000.0000.0080.0000.0000.0000.0000.0000.0000.0070.0090.0000.0000.0000.0090.0000.0000.0000.0020.0000.0000.0001.0000.0000.0000.000
tolbutamide0.0000.0000.0000.0040.0130.0140.0000.0000.0070.0100.0100.0000.0000.0000.0020.0000.0000.0000.0000.0130.0000.0050.0000.0000.0000.0000.0060.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.000
troglitazone0.0000.0000.0000.0000.0000.0000.0030.0000.0000.0110.0130.0040.0050.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0030.0130.0000.0001.0000.000
weight0.0170.0000.0000.0310.0430.0260.0480.0000.0360.0160.0200.0270.0070.0000.0130.0000.0040.0000.0550.0230.0510.0140.0000.0000.0000.0040.0380.0080.0110.0220.0000.0140.0190.0370.0540.0190.0360.0350.0000.0040.0100.0000.0000.0001.000

Missing values

2024-07-01T03:19:14.241248image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-07-01T03:19:15.890164image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

encounter_idpatient_nbrracegenderageweightadmission_type_iddischarge_disposition_idadmission_source_idtime_in_hospitalpayer_codemedical_specialtynum_lab_proceduresnum_proceduresnum_medicationsnumber_outpatientnumber_emergencynumber_inpatientdiag_1diag_2diag_3number_diagnosesmax_glu_serumA1Cresultmetforminrepaglinidenateglinidechlorpropamideglimepirideacetohexamideglipizideglyburidetolbutamidepioglitazonerosiglitazoneacarbosemiglitoltroglitazonetolazamideexamidecitogliptoninsulinglyburide-metforminglipizide-metforminglimepiride-pioglitazonemetformin-rosiglitazonemetformin-pioglitazonechangediabetesMedreadmitted
022783928222157CaucasianFemale[0-10)Missing62511MissingPediatrics-Endocrinology4101000250.83MissingMissing1No TestNo TestNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNO
114919055629189CaucasianFemale[10-20)Missing1173MissingMissing59018000276250.012559No TestNo TestNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoUpNoNoNoNoNoChYes>30
26441086047875AfricanAmericanFemale[20-30)Missing1172MissingMissing11513201648250V276No TestNo TestNoNoNoNoNoNoSteadyNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoYesNO
350036482442376CaucasianMale[30-40)Missing1172MissingMissing441160008250.434037No TestNo TestNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoUpNoNoNoNoNoChYesNO
41668042519267CaucasianMale[40-50)Missing1171MissingMissing51080001971572505No TestNo TestNoNoNoNoNoNoSteadyNoNoNoNoNoNoNoNoNoNoSteadyNoNoNoNoNoChYesNO
53575482637451CaucasianMale[50-60)Missing2123MissingMissing316160004144112509No TestNo TestNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoSteadyNoNoNoNoNoNoYes>30
65584284259809CaucasianMale[60-70)Missing3124MissingMissing70121000414411V457No TestNo TestSteadyNoNoNoSteadyNoNoNoNoNoNoNoNoNoNoNoNoSteadyNoNoNoNoNoChYesNO
763768114882984CaucasianMale[70-80)Missing1175MissingMissing730120004284922508No TestNo TestNoNoNoNoNoNoNoSteadyNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoYes>30
81252248330783CaucasianFemale[80-90)Missing21413MissingMissing68228000398427388No TestNo TestNoNoNoNoNoNoSteadyNoNoNoNoNoNoNoNoNoNoSteadyNoNoNoNoNoChYesNO
91573863555939CaucasianFemale[90-100)Missing33412MissingInternalMedicine333180004341984868No TestNo TestNoNoNoNoNoNoNoNoNoNoSteadyNoNoNoNoNoNoSteadyNoNoNoNoNoChYesNO
encounter_idpatient_nbrracegenderageweightadmission_type_iddischarge_disposition_idadmission_source_idtime_in_hospitalpayer_codemedical_specialtynum_lab_proceduresnum_proceduresnum_medicationsnumber_outpatientnumber_emergencynumber_inpatientdiag_1diag_2diag_3number_diagnosesmax_glu_serumA1Cresultmetforminrepaglinidenateglinidechlorpropamideglimepirideacetohexamideglipizideglyburidetolbutamidepioglitazonerosiglitazoneacarbosemiglitoltroglitazonetolazamideexamidecitogliptoninsulinglyburide-metforminglipizide-metforminglimepiride-pioglitazonemetformin-rosiglitazonemetformin-pioglitazonechangediabetesMedreadmitted
101756443842070140199494OtherFemale[60-70)Missing1172MDMissing466171119965854039No TestNo TestNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoSteadyNoNoNoNoNoNoYes>30
101757443842136181593374CaucasianFemale[70-80)Missing1175MissingMissing211160014915185119No TestNo TestNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoSteadyNoNoNoNoNoNoYesNO
101758443842340120975314CaucasianFemale[80-90)Missing1175MCMissing7612201029283049No TestNo TestNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoUpNoNoNoNoNoChYesNO
10175944384277886472243CaucasianMale[80-90)Missing1171MCMissing10153004357842507No TestNo TestNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoUpNoNoNoNoNoChYesNO
10176044384717650375628AfricanAmericanFemale[60-70)Missing1176DMMissing451253123454384129No TestNo TestNoNoNoNoNoNoNoNoNoNoSteadyNoNoNoNoNoNoDownNoNoNoNoNoChYes>30
101761443847548100162476AfricanAmericanMale[70-80)Missing1373MCMissing51016000250.132914589No Test>8SteadyNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoDownNoNoNoNoNoChYes>30
10176244384778274694222AfricanAmericanFemale[80-90)Missing1455MCMissing333180015602767879No TestNo TestNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoSteadyNoNoNoNoNoNoYesNO
10176344385414841088789CaucasianMale[70-80)Missing1171MCMissing53091003859029613No TestNo TestSteadyNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoDownNoNoNoNoNoChYesNO
10176444385716631693671CaucasianFemale[80-90)Missing23710MCSurgery-General452210019962859989No TestNo TestNoNoNoNoNoNoSteadyNoNoSteadyNoNoNoNoNoNoNoUpNoNoNoNoNoChYesNO
101765443867222175429310CaucasianMale[70-80)Missing1176MissingMissing13330005305307879No TestNo TestNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNO